The Experts below are selected from a list of 35334 Experts worldwide ranked by ideXlab platform
Don Towsley - One of the best experts on this subject based on the ideXlab platform.
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Online Dating recommendations matching markets and learning preferences
The Web Conference, 2014Co-Authors: Bruno Ribeiro, Hua Jiang, Benyuan Liu, Don Towsley, David Jensen, Xiaodong WangAbstract:Recommendation systems for Online Dating have recently attracted much attention from the research community. In this paper we propose a two-side matching framework for Online Dating recommendations and design an Latent Dirichlet Allocation (LDA) model to learn the user preferences from the observed user messaging behavior and user profile features. Experimental results using data from a large Online Dating website shows that two-sided matching improves the rate of successful matches by as much as 45%. Finally, using simulated matching, we show that the LDA model can correctly capture user preferences.
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Online Dating Recommendations: Matching Markets and Learning Preferences
arXiv: Social and Information Networks, 2014Co-Authors: Bruno Ribeiro, Hua Jiang, Xiaodong Wang, Benyuan Liu, David Jensen, Don TowsleyAbstract:Recommendation systems for Online Dating have recently attracted much attention from the research community. In this paper we proposed a two-side matching framework for Online Dating recommendations and design an LDA model to learn the user preferences from the observed user messaging behavior and user profile features. Experimental results using data from a large Online Dating website shows that two-sided matching improves significantly the rate of successful matches by as much as 45%. Finally, using simulated matchings we show that the the LDA model can correctly capture user preferences.
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characterization of user Online Dating behavior and preference on a large Online Dating site
Social Network Analysis, 2014Co-Authors: Peng Xia, Bruno Ribeiro, Hua Jiang, Xiaodong Wang, Cindy X Chen, Benyuan Liu, Don TowsleyAbstract:Online Dating sites have become popular platforms for people to look for romantic partners, providing an unprecedented level of access to potential dates that is otherwise not available through traditional means. Characterization of the user Online Dating behavior helps us to obtain a deep understanding of their Dating preference and make better recommendations on potential dates. In this paper we study the user Online Dating behavior and preference using a large real-world dataset from a major Online Dating site in China. In particular, we characterize the temporal behavior, message send and reply behavior of users, study how users Online Dating behaviors correlate with various user attributes, and investigate how users’ actual Online Dating behaviors deviate from their stated preferences. Our results show that on average a male sends out more messages but receives fewer messages than a female. A female is more likely to be contacted but less likely to reply to a message than a male. The number of messages that a user sends out and receives per week quickly decreases with time, especially for female users. Most messages are replied to within a short time frame with a median delay of around 9 h. Many of the user messaging behaviors align with notions in social and evolutionary psychology: males tend to look for younger females while females place more emphasis on the socioeconomic status (e.g., income, education level) of a potential date. The geographic distance between two users and the photo count of users play an important role in their Dating behavior. We show that it is important to differentiate between users’ true preferences and random selection. Some user behaviors in choosing attributes in a potential date may largely be a result of random selection. We also find that while both males and females are more likely to reply to users whose attributes come closest to the stated preferences of the receivers, there is significant discrepancy between a user’s stated Dating preference and his/her actual Online Dating behavior. We further characterize how users actual Dating behavior deviate from their stated preference. These results can provide valuable guidelines to the design of a recommendation engine for potential dates.
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Social Network Analysis - Characterization of User Online Dating Behavior and Preference on a Large Online Dating Site
Lecture Notes in Social Networks, 2014Co-Authors: Peng Xia, Bruno Ribeiro, Hua Jiang, Xiaodong Wang, Cindy X Chen, Benyuan Liu, Don TowsleyAbstract:Online Dating sites have become popular platforms for people to look for romantic partners, providing an unprecedented level of access to potential dates that is otherwise not available through traditional means. Characterization of the user Online Dating behavior helps us to obtain a deep understanding of their Dating preference and make better recommendations on potential dates. In this paper we study the user Online Dating behavior and preference using a large real-world dataset from a major Online Dating site in China. In particular, we characterize the temporal behavior, message send and reply behavior of users, study how users Online Dating behaviors correlate with various user attributes, and investigate how users’ actual Online Dating behaviors deviate from their stated preferences. Our results show that on average a male sends out more messages but receives fewer messages than a female. A female is more likely to be contacted but less likely to reply to a message than a male. The number of messages that a user sends out and receives per week quickly decreases with time, especially for female users. Most messages are replied to within a short time frame with a median delay of around 9 h. Many of the user messaging behaviors align with notions in social and evolutionary psychology: males tend to look for younger females while females place more emphasis on the socioeconomic status (e.g., income, education level) of a potential date. The geographic distance between two users and the photo count of users play an important role in their Dating behavior. We show that it is important to differentiate between users’ true preferences and random selection. Some user behaviors in choosing attributes in a potential date may largely be a result of random selection. We also find that while both males and females are more likely to reply to users whose attributes come closest to the stated preferences of the receivers, there is significant discrepancy between a user’s stated Dating preference and his/her actual Online Dating behavior. We further characterize how users actual Dating behavior deviate from their stated preference. These results can provide valuable guidelines to the design of a recommendation engine for potential dates.
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a study of user behavior on an Online Dating site
Advances in Social Networks Analysis and Mining, 2013Co-Authors: Peng Xia, Bruno Ribeiro, Cindy X Chen, Benyuan Liu, Don TowsleyAbstract:Online Dating sites have become popular platforms for people to look for potential romantic partners. It is important to understand users' Dating preferences in order to make better recommendations on potential dates. The message sending and replying actions of a user are strong indicators for what he/she is looking for in a potential date and reflect the user's actual Dating preferences. We study how users' Online Dating behaviors correlate with various user attributes using a real-world dateset from a major Online Dating site in China. Our study provides a firsthand account of the user Online Dating behaviors in China, a country with a large population and unique culture. The results can provide valuable guidelines to the design of recommendation engine for potential dates.
Peng Xia - One of the best experts on this subject based on the ideXlab platform.
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Design of reciprocal recommendation systems for Online Dating
Social Network Analysis and Mining, 2016Co-Authors: Peng Xia, Benyuan Liu, Yizhou Sun, Shuangfei Zhai, Cindy X ChenAbstract:Online Dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos) with a user’s interests, a recommendation system for Online Dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the Online Dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential Dating candidate is computed, and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major Online Dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious toward their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line.
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ASONAM - Reciprocal Recommendation System for Online Dating
Proceedings of the 2015 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining 2015, 2015Co-Authors: Peng Xia, Benyuan Liu, Yizhou Sun, Cindy X ChenAbstract:Online Dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos, etc) with a user's interests, a recommendation system for Online Dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the Online Dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential Dating candidate is computed and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major Online Dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious towards their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line.
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Reciprocal Recommendation System for Online Dating
arXiv: Social and Information Networks, 2015Co-Authors: Peng Xia, Benyuan Liu, Yizhou Sun, Cindy X ChenAbstract:Online Dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos, etc) with a user's interests, a recommendation system for Online Dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the Online Dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential Dating candidate is computed and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major Online Dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious towards their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line.
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characterization of user Online Dating behavior and preference on a large Online Dating site
Social Network Analysis, 2014Co-Authors: Peng Xia, Bruno Ribeiro, Hua Jiang, Xiaodong Wang, Cindy X Chen, Benyuan Liu, Don TowsleyAbstract:Online Dating sites have become popular platforms for people to look for romantic partners, providing an unprecedented level of access to potential dates that is otherwise not available through traditional means. Characterization of the user Online Dating behavior helps us to obtain a deep understanding of their Dating preference and make better recommendations on potential dates. In this paper we study the user Online Dating behavior and preference using a large real-world dataset from a major Online Dating site in China. In particular, we characterize the temporal behavior, message send and reply behavior of users, study how users Online Dating behaviors correlate with various user attributes, and investigate how users’ actual Online Dating behaviors deviate from their stated preferences. Our results show that on average a male sends out more messages but receives fewer messages than a female. A female is more likely to be contacted but less likely to reply to a message than a male. The number of messages that a user sends out and receives per week quickly decreases with time, especially for female users. Most messages are replied to within a short time frame with a median delay of around 9 h. Many of the user messaging behaviors align with notions in social and evolutionary psychology: males tend to look for younger females while females place more emphasis on the socioeconomic status (e.g., income, education level) of a potential date. The geographic distance between two users and the photo count of users play an important role in their Dating behavior. We show that it is important to differentiate between users’ true preferences and random selection. Some user behaviors in choosing attributes in a potential date may largely be a result of random selection. We also find that while both males and females are more likely to reply to users whose attributes come closest to the stated preferences of the receivers, there is significant discrepancy between a user’s stated Dating preference and his/her actual Online Dating behavior. We further characterize how users actual Dating behavior deviate from their stated preference. These results can provide valuable guidelines to the design of a recommendation engine for potential dates.
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Social Network Analysis - Characterization of User Online Dating Behavior and Preference on a Large Online Dating Site
Lecture Notes in Social Networks, 2014Co-Authors: Peng Xia, Bruno Ribeiro, Hua Jiang, Xiaodong Wang, Cindy X Chen, Benyuan Liu, Don TowsleyAbstract:Online Dating sites have become popular platforms for people to look for romantic partners, providing an unprecedented level of access to potential dates that is otherwise not available through traditional means. Characterization of the user Online Dating behavior helps us to obtain a deep understanding of their Dating preference and make better recommendations on potential dates. In this paper we study the user Online Dating behavior and preference using a large real-world dataset from a major Online Dating site in China. In particular, we characterize the temporal behavior, message send and reply behavior of users, study how users Online Dating behaviors correlate with various user attributes, and investigate how users’ actual Online Dating behaviors deviate from their stated preferences. Our results show that on average a male sends out more messages but receives fewer messages than a female. A female is more likely to be contacted but less likely to reply to a message than a male. The number of messages that a user sends out and receives per week quickly decreases with time, especially for female users. Most messages are replied to within a short time frame with a median delay of around 9 h. Many of the user messaging behaviors align with notions in social and evolutionary psychology: males tend to look for younger females while females place more emphasis on the socioeconomic status (e.g., income, education level) of a potential date. The geographic distance between two users and the photo count of users play an important role in their Dating behavior. We show that it is important to differentiate between users’ true preferences and random selection. Some user behaviors in choosing attributes in a potential date may largely be a result of random selection. We also find that while both males and females are more likely to reply to users whose attributes come closest to the stated preferences of the receivers, there is significant discrepancy between a user’s stated Dating preference and his/her actual Online Dating behavior. We further characterize how users actual Dating behavior deviate from their stated preference. These results can provide valuable guidelines to the design of a recommendation engine for potential dates.
Cindy X Chen - One of the best experts on this subject based on the ideXlab platform.
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Design of reciprocal recommendation systems for Online Dating
Social Network Analysis and Mining, 2016Co-Authors: Peng Xia, Benyuan Liu, Yizhou Sun, Shuangfei Zhai, Cindy X ChenAbstract:Online Dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos) with a user’s interests, a recommendation system for Online Dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the Online Dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential Dating candidate is computed, and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major Online Dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious toward their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line.
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ASONAM - Reciprocal Recommendation System for Online Dating
Proceedings of the 2015 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining 2015, 2015Co-Authors: Peng Xia, Benyuan Liu, Yizhou Sun, Cindy X ChenAbstract:Online Dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos, etc) with a user's interests, a recommendation system for Online Dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the Online Dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential Dating candidate is computed and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major Online Dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious towards their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line.
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Reciprocal Recommendation System for Online Dating
arXiv: Social and Information Networks, 2015Co-Authors: Peng Xia, Benyuan Liu, Yizhou Sun, Cindy X ChenAbstract:Online Dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos, etc) with a user's interests, a recommendation system for Online Dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the Online Dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential Dating candidate is computed and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major Online Dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious towards their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line.
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characterization of user Online Dating behavior and preference on a large Online Dating site
Social Network Analysis, 2014Co-Authors: Peng Xia, Bruno Ribeiro, Hua Jiang, Xiaodong Wang, Cindy X Chen, Benyuan Liu, Don TowsleyAbstract:Online Dating sites have become popular platforms for people to look for romantic partners, providing an unprecedented level of access to potential dates that is otherwise not available through traditional means. Characterization of the user Online Dating behavior helps us to obtain a deep understanding of their Dating preference and make better recommendations on potential dates. In this paper we study the user Online Dating behavior and preference using a large real-world dataset from a major Online Dating site in China. In particular, we characterize the temporal behavior, message send and reply behavior of users, study how users Online Dating behaviors correlate with various user attributes, and investigate how users’ actual Online Dating behaviors deviate from their stated preferences. Our results show that on average a male sends out more messages but receives fewer messages than a female. A female is more likely to be contacted but less likely to reply to a message than a male. The number of messages that a user sends out and receives per week quickly decreases with time, especially for female users. Most messages are replied to within a short time frame with a median delay of around 9 h. Many of the user messaging behaviors align with notions in social and evolutionary psychology: males tend to look for younger females while females place more emphasis on the socioeconomic status (e.g., income, education level) of a potential date. The geographic distance between two users and the photo count of users play an important role in their Dating behavior. We show that it is important to differentiate between users’ true preferences and random selection. Some user behaviors in choosing attributes in a potential date may largely be a result of random selection. We also find that while both males and females are more likely to reply to users whose attributes come closest to the stated preferences of the receivers, there is significant discrepancy between a user’s stated Dating preference and his/her actual Online Dating behavior. We further characterize how users actual Dating behavior deviate from their stated preference. These results can provide valuable guidelines to the design of a recommendation engine for potential dates.
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Social Network Analysis - Characterization of User Online Dating Behavior and Preference on a Large Online Dating Site
Lecture Notes in Social Networks, 2014Co-Authors: Peng Xia, Bruno Ribeiro, Hua Jiang, Xiaodong Wang, Cindy X Chen, Benyuan Liu, Don TowsleyAbstract:Online Dating sites have become popular platforms for people to look for romantic partners, providing an unprecedented level of access to potential dates that is otherwise not available through traditional means. Characterization of the user Online Dating behavior helps us to obtain a deep understanding of their Dating preference and make better recommendations on potential dates. In this paper we study the user Online Dating behavior and preference using a large real-world dataset from a major Online Dating site in China. In particular, we characterize the temporal behavior, message send and reply behavior of users, study how users Online Dating behaviors correlate with various user attributes, and investigate how users’ actual Online Dating behaviors deviate from their stated preferences. Our results show that on average a male sends out more messages but receives fewer messages than a female. A female is more likely to be contacted but less likely to reply to a message than a male. The number of messages that a user sends out and receives per week quickly decreases with time, especially for female users. Most messages are replied to within a short time frame with a median delay of around 9 h. Many of the user messaging behaviors align with notions in social and evolutionary psychology: males tend to look for younger females while females place more emphasis on the socioeconomic status (e.g., income, education level) of a potential date. The geographic distance between two users and the photo count of users play an important role in their Dating behavior. We show that it is important to differentiate between users’ true preferences and random selection. Some user behaviors in choosing attributes in a potential date may largely be a result of random selection. We also find that while both males and females are more likely to reply to users whose attributes come closest to the stated preferences of the receivers, there is significant discrepancy between a user’s stated Dating preference and his/her actual Online Dating behavior. We further characterize how users actual Dating behavior deviate from their stated preference. These results can provide valuable guidelines to the design of a recommendation engine for potential dates.
Benyuan Liu - One of the best experts on this subject based on the ideXlab platform.
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Design of reciprocal recommendation systems for Online Dating
Social Network Analysis and Mining, 2016Co-Authors: Peng Xia, Benyuan Liu, Yizhou Sun, Shuangfei Zhai, Cindy X ChenAbstract:Online Dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos) with a user’s interests, a recommendation system for Online Dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the Online Dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential Dating candidate is computed, and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major Online Dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious toward their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line.
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ASONAM - Reciprocal Recommendation System for Online Dating
Proceedings of the 2015 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining 2015, 2015Co-Authors: Peng Xia, Benyuan Liu, Yizhou Sun, Cindy X ChenAbstract:Online Dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos, etc) with a user's interests, a recommendation system for Online Dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the Online Dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential Dating candidate is computed and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major Online Dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious towards their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line.
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Reciprocal Recommendation System for Online Dating
arXiv: Social and Information Networks, 2015Co-Authors: Peng Xia, Benyuan Liu, Yizhou Sun, Cindy X ChenAbstract:Online Dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos, etc) with a user's interests, a recommendation system for Online Dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the Online Dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential Dating candidate is computed and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major Online Dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious towards their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line.
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Online Dating recommendations matching markets and learning preferences
The Web Conference, 2014Co-Authors: Bruno Ribeiro, Hua Jiang, Benyuan Liu, Don Towsley, David Jensen, Xiaodong WangAbstract:Recommendation systems for Online Dating have recently attracted much attention from the research community. In this paper we propose a two-side matching framework for Online Dating recommendations and design an Latent Dirichlet Allocation (LDA) model to learn the user preferences from the observed user messaging behavior and user profile features. Experimental results using data from a large Online Dating website shows that two-sided matching improves the rate of successful matches by as much as 45%. Finally, using simulated matching, we show that the LDA model can correctly capture user preferences.
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Online Dating Recommendations: Matching Markets and Learning Preferences
arXiv: Social and Information Networks, 2014Co-Authors: Bruno Ribeiro, Hua Jiang, Xiaodong Wang, Benyuan Liu, David Jensen, Don TowsleyAbstract:Recommendation systems for Online Dating have recently attracted much attention from the research community. In this paper we proposed a two-side matching framework for Online Dating recommendations and design an LDA model to learn the user preferences from the observed user messaging behavior and user profile features. Experimental results using data from a large Online Dating website shows that two-sided matching improves significantly the rate of successful matches by as much as 45%. Finally, using simulated matchings we show that the the LDA model can correctly capture user preferences.
Bruno Ribeiro - One of the best experts on this subject based on the ideXlab platform.
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Online Dating recommendations matching markets and learning preferences
The Web Conference, 2014Co-Authors: Bruno Ribeiro, Hua Jiang, Benyuan Liu, Don Towsley, David Jensen, Xiaodong WangAbstract:Recommendation systems for Online Dating have recently attracted much attention from the research community. In this paper we propose a two-side matching framework for Online Dating recommendations and design an Latent Dirichlet Allocation (LDA) model to learn the user preferences from the observed user messaging behavior and user profile features. Experimental results using data from a large Online Dating website shows that two-sided matching improves the rate of successful matches by as much as 45%. Finally, using simulated matching, we show that the LDA model can correctly capture user preferences.
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Online Dating Recommendations: Matching Markets and Learning Preferences
arXiv: Social and Information Networks, 2014Co-Authors: Bruno Ribeiro, Hua Jiang, Xiaodong Wang, Benyuan Liu, David Jensen, Don TowsleyAbstract:Recommendation systems for Online Dating have recently attracted much attention from the research community. In this paper we proposed a two-side matching framework for Online Dating recommendations and design an LDA model to learn the user preferences from the observed user messaging behavior and user profile features. Experimental results using data from a large Online Dating website shows that two-sided matching improves significantly the rate of successful matches by as much as 45%. Finally, using simulated matchings we show that the the LDA model can correctly capture user preferences.
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characterization of user Online Dating behavior and preference on a large Online Dating site
Social Network Analysis, 2014Co-Authors: Peng Xia, Bruno Ribeiro, Hua Jiang, Xiaodong Wang, Cindy X Chen, Benyuan Liu, Don TowsleyAbstract:Online Dating sites have become popular platforms for people to look for romantic partners, providing an unprecedented level of access to potential dates that is otherwise not available through traditional means. Characterization of the user Online Dating behavior helps us to obtain a deep understanding of their Dating preference and make better recommendations on potential dates. In this paper we study the user Online Dating behavior and preference using a large real-world dataset from a major Online Dating site in China. In particular, we characterize the temporal behavior, message send and reply behavior of users, study how users Online Dating behaviors correlate with various user attributes, and investigate how users’ actual Online Dating behaviors deviate from their stated preferences. Our results show that on average a male sends out more messages but receives fewer messages than a female. A female is more likely to be contacted but less likely to reply to a message than a male. The number of messages that a user sends out and receives per week quickly decreases with time, especially for female users. Most messages are replied to within a short time frame with a median delay of around 9 h. Many of the user messaging behaviors align with notions in social and evolutionary psychology: males tend to look for younger females while females place more emphasis on the socioeconomic status (e.g., income, education level) of a potential date. The geographic distance between two users and the photo count of users play an important role in their Dating behavior. We show that it is important to differentiate between users’ true preferences and random selection. Some user behaviors in choosing attributes in a potential date may largely be a result of random selection. We also find that while both males and females are more likely to reply to users whose attributes come closest to the stated preferences of the receivers, there is significant discrepancy between a user’s stated Dating preference and his/her actual Online Dating behavior. We further characterize how users actual Dating behavior deviate from their stated preference. These results can provide valuable guidelines to the design of a recommendation engine for potential dates.
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Social Network Analysis - Characterization of User Online Dating Behavior and Preference on a Large Online Dating Site
Lecture Notes in Social Networks, 2014Co-Authors: Peng Xia, Bruno Ribeiro, Hua Jiang, Xiaodong Wang, Cindy X Chen, Benyuan Liu, Don TowsleyAbstract:Online Dating sites have become popular platforms for people to look for romantic partners, providing an unprecedented level of access to potential dates that is otherwise not available through traditional means. Characterization of the user Online Dating behavior helps us to obtain a deep understanding of their Dating preference and make better recommendations on potential dates. In this paper we study the user Online Dating behavior and preference using a large real-world dataset from a major Online Dating site in China. In particular, we characterize the temporal behavior, message send and reply behavior of users, study how users Online Dating behaviors correlate with various user attributes, and investigate how users’ actual Online Dating behaviors deviate from their stated preferences. Our results show that on average a male sends out more messages but receives fewer messages than a female. A female is more likely to be contacted but less likely to reply to a message than a male. The number of messages that a user sends out and receives per week quickly decreases with time, especially for female users. Most messages are replied to within a short time frame with a median delay of around 9 h. Many of the user messaging behaviors align with notions in social and evolutionary psychology: males tend to look for younger females while females place more emphasis on the socioeconomic status (e.g., income, education level) of a potential date. The geographic distance between two users and the photo count of users play an important role in their Dating behavior. We show that it is important to differentiate between users’ true preferences and random selection. Some user behaviors in choosing attributes in a potential date may largely be a result of random selection. We also find that while both males and females are more likely to reply to users whose attributes come closest to the stated preferences of the receivers, there is significant discrepancy between a user’s stated Dating preference and his/her actual Online Dating behavior. We further characterize how users actual Dating behavior deviate from their stated preference. These results can provide valuable guidelines to the design of a recommendation engine for potential dates.
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a study of user behavior on an Online Dating site
Advances in Social Networks Analysis and Mining, 2013Co-Authors: Peng Xia, Bruno Ribeiro, Cindy X Chen, Benyuan Liu, Don TowsleyAbstract:Online Dating sites have become popular platforms for people to look for potential romantic partners. It is important to understand users' Dating preferences in order to make better recommendations on potential dates. The message sending and replying actions of a user are strong indicators for what he/she is looking for in a potential date and reflect the user's actual Dating preferences. We study how users' Online Dating behaviors correlate with various user attributes using a real-world dateset from a major Online Dating site in China. Our study provides a firsthand account of the user Online Dating behaviors in China, a country with a large population and unique culture. The results can provide valuable guidelines to the design of recommendation engine for potential dates.
