The Experts below are selected from a list of 26184 Experts worldwide ranked by ideXlab platform
Bamshad Mobasher - One of the best experts on this subject based on the ideXlab platform.
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Web User Segmentation based on a mixture of factor analyzers
Lecture Notes in Computer Science, 2006Co-Authors: Yanzan Kevin Zhou, Bamshad MobasherAbstract:This paper proposes an approach for Web User Segmentation and online behavior analysis based on a mixture of factor analyzers (MFA). In our proposed framework, we model Users' shared interests as a set of common latent factors extracted through factor analysis, and we discover User segments based on the posterior component distribution of a finite mixture model. This allows us to measure the relationships between Users' unobserved conceptual interests and their observed navigational behavior in a principled probabilistic manner. Our experimental results show that the MFA-based approach results in finer-grained representation of User behavior and can successfully discover heterogeneous User segments and characterize these segments with respect to their common preferences.
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EC-Web - Web User Segmentation based on a mixture of factor analyzers
E-Commerce and Web Technologies, 2006Co-Authors: Yanzan Kevin Zhou, Bamshad MobasherAbstract:This paper proposes an approach for Web User Segmentation and online behavior analysis based on a mixture of factor analyzers (MFA). In our proposed framework, we model Users’ shared interests as a set of common latent factors extracted through factor analysis, and we discover User segments based on the posterior component distribution of a finite mixture model. This allows us to measure the relationships between Users’ unobserved conceptual interests and their observed navigational behavior in a principled probabilistic manner. Our experimental results show that the MFA-based approach results in finer-grained representation of User behavior and can successfully discover heterogeneous User segments and characterize these segments with respect to their common preferences.
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web usage mining based on probabilistic latent semantic analysis
Knowledge Discovery and Data Mining, 2004Co-Authors: Xin Jin, Yanzan Zhou, Bamshad MobasherAbstract:The primary goal of Web usage mining is the discovery of patterns in the navigational behavior of Web Users. Standard approaches, such as clustering of User sessions and discovering association rules or frequent navigational paths, do not generally provide the ability to automatically characterize or quantify the unobservable factors that lead to common navigational patterns. It is, therefore, necessary to develop techniques that can automatically discover hidden semantic relationships among Users as well as between Users and Web objects. Probabilistic Latent Semantic Analysis (PLSA) is particularly useful in this context, since it can uncover latent semantic associations among Users and pages based on the co-occurrence patterns of these pages in User sessions. In this paper, we develop a unified framework for the discovery and analysis of Web navigational patterns based on PLSA. We show the flexibility of this framework in characterizing various relationships among Users and Web objects. Since these relationships are measured in terms of probabilities, we are able to use probabilistic inference to perform a variety of analysis tasks such as User Segmentation, page classification, as well as predictive tasks such as collaborative recommendations. We demonstrate the effectiveness of our approach through experiments performed on real-world data sets.
Zheng Chen - One of the best experts on this subject based on the ideXlab platform.
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WWW - Transfer learning for behavioral targeting
Proceedings of the 19th international conference on World wide web - WWW '10, 2010Co-Authors: Tianqi Chen, Zheng ChenAbstract:Recently, Behavioral Targeting (BT) is attracting much attention from both industry and academia due to its rapid growth in online advertising market. Though a basic assumption of BT, which is, the Users who share similar Web browsing behaviors will have similar preference over ads, has been empirically verified, we argue that the Users' ad click preference and Web browsing behavior are not reflecting the same User intent though they are correlated. In this paper, we propose to formulate BT as a transfer learning problem. We treat the Users' preference over ads and Web browsing behaviors as two different User behavioral domains and propose to utilize transfer learning strategy across these two User behavioral domains to segment Users for BT ads delivery. We show that some classical BT solutions could be formulated in transfer learning view. As an example, we propose to leverage translated learning, which is a recent proposed transfer learning algorithm, to benefit the BT ads delivery. Experimental results on real ad click data show that, BT User Segmentation by the approach of transfer learning can outperform the classical User Segmentation strategies for larger than 20% in terms of smoothed ad Click Through Rate(CTR).
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Probabilistic latent semantic User Segmentation for behavioral targeted advertising
Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising - ADKDD '09, 2009Co-Authors: Xiaohui Wu, Shuicheng Yan, Jun Yan, Ning Liu, Ying Chen, Zheng ChenAbstract:Behavioral Targeting (BT), which aims to deliver the most appropriate advertisements to the most appropriate Users, is attracting much attention in online advertising market. A key challenge of BT is how to automatically segment Users for ads delivery, and good User Segmentation may significantly improve the ad click-through rate (CTR). Different from classical User Segmentation strategies, which rarely take the semantics of User behaviors into consideration, we propose in this paper a novel User Segmentation algorithm named Probabilistic Latent Semantic User Segmentation (PLSUS). PLSUS adopts the probabilistic latent semantic analysis to mine the relationship between Users and their behaviors so as to segment Users in a semantic manner. We perform experiments on the real world ad click through log of a commercial search engine. Comparing with the other two classical clustering algorithms, K-Means and CLUTO, PLSUS can further improve the ads CTR up to 100%. To our best knowledge, this work is an early semantic User Segmentation study for BT in academia.
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KDD Workshop on Data Mining and Audience Intelligence for Advertising - Probabilistic latent semantic User Segmentation for behavioral targeted advertising
Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising - ADKDD '09, 2009Co-Authors: Jun Yan, Shuicheng Yan, Ning Liu, Ying Chen, Zheng ChenAbstract:Behavioral Targeting (BT), which aims to deliver the most appropriate advertisements to the most appropriate Users, is attracting much attention in online advertising market. A key challenge of BT is how to automatically segment Users for ads delivery, and good User Segmentation may significantly improve the ad click-through rate (CTR). Different from classical User Segmentation strategies, which rarely take the semantics of User behaviors into consideration, we propose in this paper a novel User Segmentation algorithm named Probabilistic Latent Semantic User Segmentation (PLSUS). PLSUS adopts the probabilistic latent semantic analysis to mine the relationship between Users and their behaviors so as to segment Users in a semantic manner. We perform experiments on the real world ad click through log of a commercial search engine. Comparing with the other two classical clustering algorithms, K-Means and CLUTO, PLSUS can further improve the ads CTR up to 100%. To our best knowledge, this work is an early semantic User Segmentation study for BT in academia.
Boming Xu - One of the best experts on this subject based on the ideXlab platform.
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HCI (20) - Human Factors Design Research with Persona for Kids Furniture in Shanghai Middle-Class Family
Lecture Notes in Computer Science, 2013Co-Authors: Boming XuAbstract:There is a huge market for Chinese kids furniture, which, however, is still designed on the level of traditional Ergonomics. The paper, targeted at Shanghai middle-class family, analyzes the correlation between the needs, purpose, behavior and viewpoints of multi-Users, based on the data collected through Ethnography. With many factors such as family structure, environmental factor and educational notion taken into account, it constructs User Segmentation of multi-Users’ kids furniture in terms of persona and accordingly gives suggestions on Human Factors Design of kids furniture.
Yanzan Kevin Zhou - One of the best experts on this subject based on the ideXlab platform.
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Web User Segmentation based on a mixture of factor analyzers
Lecture Notes in Computer Science, 2006Co-Authors: Yanzan Kevin Zhou, Bamshad MobasherAbstract:This paper proposes an approach for Web User Segmentation and online behavior analysis based on a mixture of factor analyzers (MFA). In our proposed framework, we model Users' shared interests as a set of common latent factors extracted through factor analysis, and we discover User segments based on the posterior component distribution of a finite mixture model. This allows us to measure the relationships between Users' unobserved conceptual interests and their observed navigational behavior in a principled probabilistic manner. Our experimental results show that the MFA-based approach results in finer-grained representation of User behavior and can successfully discover heterogeneous User segments and characterize these segments with respect to their common preferences.
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EC-Web - Web User Segmentation based on a mixture of factor analyzers
E-Commerce and Web Technologies, 2006Co-Authors: Yanzan Kevin Zhou, Bamshad MobasherAbstract:This paper proposes an approach for Web User Segmentation and online behavior analysis based on a mixture of factor analyzers (MFA). In our proposed framework, we model Users’ shared interests as a set of common latent factors extracted through factor analysis, and we discover User segments based on the posterior component distribution of a finite mixture model. This allows us to measure the relationships between Users’ unobserved conceptual interests and their observed navigational behavior in a principled probabilistic manner. Our experimental results show that the MFA-based approach results in finer-grained representation of User behavior and can successfully discover heterogeneous User segments and characterize these segments with respect to their common preferences.
Aoying Zhou - One of the best experts on this subject based on the ideXlab platform.
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search behavior based latent semantic User Segmentation for advertising targeting
International Conference on Data Mining, 2013Co-Authors: Xueqing Gong, Xinyu Guo, Rong Zhang, Aoying ZhouAbstract:The popularity of internet usage greatly motivates the online advertising activities. Compared to advertising on traditional media, online advertising has rich information as well as necessary techniques to achieve precise User targeting. This rich information includes the search behaviors of a User, such as queries issued, or the ads clicked by the User. For popular websites with large number of active Users, ad delivery targeting at individual Users puts too much burden on the system. User Segmentation is an alternative way to relieve this burden by grouping Users of similar interests together, then the ad delivery system targets the User segments to display relevant ads, instead of individual Users. Existing User Segmentation work either adapts clustering methods without considering the hidden semantics embedded in the data, such as K-means, or treats Users as data instance and clusters Users indirectly even if the latent semantics is incorporated into the transformed data, such as PLSA or LDA. In this paper, we present a search behavior based latent semantic User Segmentation method and validate its effectiveness on new ads. Instead of treating Users as data instances, they are used as attributes of User issued queries or clicked ads which are considered to be data instances. LDA is then applied to this data set to directly obtain the User segments. Compared to popular K-means clustering, our approach achieves higher CTR values on new ads, with only simple search information.
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ICDM - Search Behavior Based Latent Semantic User Segmentation for Advertising Targeting
2013 IEEE 13th International Conference on Data Mining, 2013Co-Authors: Xueqing Gong, Xinyu Guo, Rong Zhang, Aoying ZhouAbstract:The popularity of internet usage greatly motivates the online advertising activities. Compared to advertising on traditional media, online advertising has rich information as well as necessary techniques to achieve precise User targeting. This rich information includes the search behaviors of a User, such as queries issued, or the ads clicked by the User. For popular websites with large number of active Users, ad delivery targeting at individual Users puts too much burden on the system. User Segmentation is an alternative way to relieve this burden by grouping Users of similar interests together, then the ad delivery system targets the User segments to display relevant ads, instead of individual Users. Existing User Segmentation work either adapts clustering methods without considering the hidden semantics embedded in the data, such as K-means, or treats Users as data instance and clusters Users indirectly even if the latent semantics is incorporated into the transformed data, such as PLSA or LDA. In this paper, we present a search behavior based latent semantic User Segmentation method and validate its effectiveness on new ads. Instead of treating Users as data instances, they are used as attributes of User issued queries or clicked ads which are considered to be data instances. LDA is then applied to this data set to directly obtain the User segments. Compared to popular K-means clustering, our approach achieves higher CTR values on new ads, with only simple search information.
