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Venky Harinarayan - One of the best experts on this subject based on the ideXlab platform.

  • Anatomy of a gift Recommendation Engine powered by social media
    Proceedings of the 2012 international conference on Management of Data - SIGMOD '12, 2012
    Co-Authors: Yannis Pavlidis, Madhusudan Mathihalli, Indrani Chakravarty, Ron Benson, Robert Yau, Mike McKiernan, Anand Rajaraman, Arvind Batra, Ravi Raj, Venky Harinarayan
    Abstract:

    More and more people conduct their shopping online [1], especially during the holiday season [2]. Shopping online offers a lot of convenience, including the luxury of shopping from home, the ease of research, better prices, and in many cases access to unique products not available in stores. One of the facets of shopping is gifting. Gifting may be the act of giving a present to somebody because of an event (e.g., birthday) or occasion (e.g., house warming party). People may also treat themselves or loved ones to a gift. Regardless of the occasion or the reason for gifting, there is often one common denominator: delight the receiver. The pursuit of delight can cause a great deal of stress and also be extremely time consuming as many people today either already have everything, or have easy access to everything. The @WalmartLabs Gift Recommendation Engine and its first application, Shopycat, which is a gift finder application on Facebook, aim to find the right and "wow" gifts much easier and quicker than ever before, by taking into account social media interactions. In this paper we will begin by describing the Shopycat Social Gift Finder Facebook application. Next, we describe the components of the Engine. Finally, we discuss the metrics used to evaluate the Engine. Building such a gift Recommendation Engine raises many challenges, in inferring user interests, computing the giftability of a product and an interest, and processing the big and fast data associated with social media. We briefly discuss our solutions to these challenges. Overall, our gift Recommendation Engine is an example that illustrates social commerce, a powerful emerging trend in e-commerce, and a major focus of @WalmartLabs. © 2012 ACM.

  • SIGMOD Conference - Anatomy of a gift Recommendation Engine powered by social media
    Proceedings of the 2012 international conference on Management of Data - SIGMOD '12, 2012
    Co-Authors: Yannis Pavlidis, Madhusudan Mathihalli, Indrani Chakravarty, Ron Benson, Robert Yau, Mike McKiernan, Venky Harinarayan, Arvind Batra, Ravi Raj, Anand Rajaraman
    Abstract:

    More and more people conduct their shopping online [1], especially during the holiday season [2]. Shopping online offers a lot of convenience, including the luxury of shopping from home, the ease of research, better prices, and in many cases access to unique products not available in stores. One of the facets of shopping is gifting. Gifting may be the act of giving a present to somebody because of an event (e.g., birthday) or occasion (e.g., house warming party). People may also treat themselves or loved ones to a gift. Regardless of the occasion or the reason for gifting, there is often one common denominator: delight the receiver. The pursuit of delight can cause a great deal of stress and also be extremely time consuming as many people today either already have everything, or have easy access to everything. The @WalmartLabs Gift Recommendation Engine and its first application, Shopycat, which is a gift finder application on Facebook, aim to find the right and "wow" gifts much easier and quicker than ever before, by taking into account social media interactions. In this paper we will begin by describing the Shopycat Social Gift Finder Facebook application. Next, we describe the components of the Engine. Finally, we discuss the metrics used to evaluate the Engine. Building such a gift Recommendation Engine raises many challenges, in inferring user interests, computing the giftability of a product and an interest, and processing the big and fast data associated with social media. We briefly discuss our solutions to these challenges. Overall, our gift Recommendation Engine is an example that illustrates social commerce, a powerful emerging trend in e-commerce, and a major focus of @WalmartLabs.

Anand Rajaraman - One of the best experts on this subject based on the ideXlab platform.

  • Anatomy of a gift Recommendation Engine powered by social media
    Proceedings of the 2012 international conference on Management of Data - SIGMOD '12, 2012
    Co-Authors: Yannis Pavlidis, Madhusudan Mathihalli, Indrani Chakravarty, Ron Benson, Robert Yau, Mike McKiernan, Anand Rajaraman, Arvind Batra, Ravi Raj, Venky Harinarayan
    Abstract:

    More and more people conduct their shopping online [1], especially during the holiday season [2]. Shopping online offers a lot of convenience, including the luxury of shopping from home, the ease of research, better prices, and in many cases access to unique products not available in stores. One of the facets of shopping is gifting. Gifting may be the act of giving a present to somebody because of an event (e.g., birthday) or occasion (e.g., house warming party). People may also treat themselves or loved ones to a gift. Regardless of the occasion or the reason for gifting, there is often one common denominator: delight the receiver. The pursuit of delight can cause a great deal of stress and also be extremely time consuming as many people today either already have everything, or have easy access to everything. The @WalmartLabs Gift Recommendation Engine and its first application, Shopycat, which is a gift finder application on Facebook, aim to find the right and "wow" gifts much easier and quicker than ever before, by taking into account social media interactions. In this paper we will begin by describing the Shopycat Social Gift Finder Facebook application. Next, we describe the components of the Engine. Finally, we discuss the metrics used to evaluate the Engine. Building such a gift Recommendation Engine raises many challenges, in inferring user interests, computing the giftability of a product and an interest, and processing the big and fast data associated with social media. We briefly discuss our solutions to these challenges. Overall, our gift Recommendation Engine is an example that illustrates social commerce, a powerful emerging trend in e-commerce, and a major focus of @WalmartLabs. © 2012 ACM.

  • SIGMOD Conference - Anatomy of a gift Recommendation Engine powered by social media
    Proceedings of the 2012 international conference on Management of Data - SIGMOD '12, 2012
    Co-Authors: Yannis Pavlidis, Madhusudan Mathihalli, Indrani Chakravarty, Ron Benson, Robert Yau, Mike McKiernan, Venky Harinarayan, Arvind Batra, Ravi Raj, Anand Rajaraman
    Abstract:

    More and more people conduct their shopping online [1], especially during the holiday season [2]. Shopping online offers a lot of convenience, including the luxury of shopping from home, the ease of research, better prices, and in many cases access to unique products not available in stores. One of the facets of shopping is gifting. Gifting may be the act of giving a present to somebody because of an event (e.g., birthday) or occasion (e.g., house warming party). People may also treat themselves or loved ones to a gift. Regardless of the occasion or the reason for gifting, there is often one common denominator: delight the receiver. The pursuit of delight can cause a great deal of stress and also be extremely time consuming as many people today either already have everything, or have easy access to everything. The @WalmartLabs Gift Recommendation Engine and its first application, Shopycat, which is a gift finder application on Facebook, aim to find the right and "wow" gifts much easier and quicker than ever before, by taking into account social media interactions. In this paper we will begin by describing the Shopycat Social Gift Finder Facebook application. Next, we describe the components of the Engine. Finally, we discuss the metrics used to evaluate the Engine. Building such a gift Recommendation Engine raises many challenges, in inferring user interests, computing the giftability of a product and an interest, and processing the big and fast data associated with social media. We briefly discuss our solutions to these challenges. Overall, our gift Recommendation Engine is an example that illustrates social commerce, a powerful emerging trend in e-commerce, and a major focus of @WalmartLabs.

Kathy J. Liszka - One of the best experts on this subject based on the ideXlab platform.

  • RedTweet: Recommendation Engine for reddit
    Journal of Intelligent Information Systems, 2016
    Co-Authors: Hoang Nguyen, Rachel Richards, Chien-chung Chan, Kathy J. Liszka
    Abstract:

    Twitter and Reddit are two of the most popular social media sites used today. In this paper, we study the use of machine learning and WordNet-based classifiers to generate an interest profile from a user's tweets and use this to recommend loosely related Reddit threads which the reader is most likely to be interested in. We introduce a genre classification algorithm using a similarity measure derived from WordNet lexical database for English to label genres for nouns in tweets. The proposed algorithm generates a user's interest profile from their tweets based on a referencing taxonomy of genres derived from the genre-tagged Brown Corpus augmented with a technology genre. The top K genres of a user's interest profile can be used for recommending subreddit articles in those genres. Experiments using real life test cases collected from Twitter have been done to compare the performance on genre classification by using the WordNet classifier and machine learning classifiers such as SVM, Random Forests, and an ensemble of Bayesian classifiers. Empirically, we have obtained similar results from the two different approaches with a sufficient number of tweets. It seems that machine learning algorithms as well as the WordNet ontology are viable tools for developing Recommendation Engine based on genre classification. One advantage of the WordNet approach is simplicity and no learning is required. However, the WordNet classifier tends to have poor precision on users with very few tweets.

  • ASONAM - RedTweet: Recommendation Engine for Reddit
    Proceedings of the 2015 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining 2015, 2015
    Co-Authors: Hoang Nguyen, Rachel Richards, Chien-chung Chan, Kathy J. Liszka
    Abstract:

    With the growing popularity in using social media to collect data, there is an increasing need to discover ways in which to productively use this data. Our objective is to form an interest profile from tweets and use this to recommend loosely related Reddit threads which the reader is most likely to be interested in. The problem is approached as a genre classification problem. Given a tweet, we want to deduce what genre(s) it might fall under if those words in the tweet were used in official texts. From there, we keep track of how many tweets fall under which genre, and generate a list of Reddit threads which similarly fall under those genre and are proportional to the interests of the user. Due to the complexity of genre classification, we chose to use an ensemble approach for classification. We use three classifiers in our ensemble: 1) a classic Naive Bayesian classifier, 2) a Naive Bayesian classifier trained only on the parts-of-speech of sentences, and 3) a Naive Bayesian classifier which will only make a decision if the probability P(x) ≥ 0.9. We measured the success of our classifiers by comparing the accuracy, precision, and recall of each model. Classifiers 1 and 2 had high accuracy than classifier 3 but classifier 3 had a much higher precision and recall rate. After creating the classifier, we were then able to form an interest profile on well-known people, one who has a small number of tweets versus one with a much larger number, and compile a list of recommended articles. The genres tagged to each person seemed to match their public personas and most of the articles chosen fit these genres. Our results are a valuable beginning for what constitutes a much larger project.

Yannis Pavlidis - One of the best experts on this subject based on the ideXlab platform.

  • Anatomy of a gift Recommendation Engine powered by social media
    Proceedings of the 2012 international conference on Management of Data - SIGMOD '12, 2012
    Co-Authors: Yannis Pavlidis, Madhusudan Mathihalli, Indrani Chakravarty, Ron Benson, Robert Yau, Mike McKiernan, Anand Rajaraman, Arvind Batra, Ravi Raj, Venky Harinarayan
    Abstract:

    More and more people conduct their shopping online [1], especially during the holiday season [2]. Shopping online offers a lot of convenience, including the luxury of shopping from home, the ease of research, better prices, and in many cases access to unique products not available in stores. One of the facets of shopping is gifting. Gifting may be the act of giving a present to somebody because of an event (e.g., birthday) or occasion (e.g., house warming party). People may also treat themselves or loved ones to a gift. Regardless of the occasion or the reason for gifting, there is often one common denominator: delight the receiver. The pursuit of delight can cause a great deal of stress and also be extremely time consuming as many people today either already have everything, or have easy access to everything. The @WalmartLabs Gift Recommendation Engine and its first application, Shopycat, which is a gift finder application on Facebook, aim to find the right and "wow" gifts much easier and quicker than ever before, by taking into account social media interactions. In this paper we will begin by describing the Shopycat Social Gift Finder Facebook application. Next, we describe the components of the Engine. Finally, we discuss the metrics used to evaluate the Engine. Building such a gift Recommendation Engine raises many challenges, in inferring user interests, computing the giftability of a product and an interest, and processing the big and fast data associated with social media. We briefly discuss our solutions to these challenges. Overall, our gift Recommendation Engine is an example that illustrates social commerce, a powerful emerging trend in e-commerce, and a major focus of @WalmartLabs. © 2012 ACM.

  • SIGMOD Conference - Anatomy of a gift Recommendation Engine powered by social media
    Proceedings of the 2012 international conference on Management of Data - SIGMOD '12, 2012
    Co-Authors: Yannis Pavlidis, Madhusudan Mathihalli, Indrani Chakravarty, Ron Benson, Robert Yau, Mike McKiernan, Venky Harinarayan, Arvind Batra, Ravi Raj, Anand Rajaraman
    Abstract:

    More and more people conduct their shopping online [1], especially during the holiday season [2]. Shopping online offers a lot of convenience, including the luxury of shopping from home, the ease of research, better prices, and in many cases access to unique products not available in stores. One of the facets of shopping is gifting. Gifting may be the act of giving a present to somebody because of an event (e.g., birthday) or occasion (e.g., house warming party). People may also treat themselves or loved ones to a gift. Regardless of the occasion or the reason for gifting, there is often one common denominator: delight the receiver. The pursuit of delight can cause a great deal of stress and also be extremely time consuming as many people today either already have everything, or have easy access to everything. The @WalmartLabs Gift Recommendation Engine and its first application, Shopycat, which is a gift finder application on Facebook, aim to find the right and "wow" gifts much easier and quicker than ever before, by taking into account social media interactions. In this paper we will begin by describing the Shopycat Social Gift Finder Facebook application. Next, we describe the components of the Engine. Finally, we discuss the metrics used to evaluate the Engine. Building such a gift Recommendation Engine raises many challenges, in inferring user interests, computing the giftability of a product and an interest, and processing the big and fast data associated with social media. We briefly discuss our solutions to these challenges. Overall, our gift Recommendation Engine is an example that illustrates social commerce, a powerful emerging trend in e-commerce, and a major focus of @WalmartLabs.

Zheng-hua Tan - One of the best experts on this subject based on the ideXlab platform.

  • AMORE: design and implementation of a commercial-strength parallel hybrid movie Recommendation Engine
    Knowledge and Information Systems, 2016
    Co-Authors: Ioannis T Christou, E. Amolochitis, Zheng-hua Tan
    Abstract:

    AMORE is a hybrid Recommendation system that provides movie recommenda- tion functionality to video-on-demand subscribers of a major triple-play service provider in Greece.Without any user relevance feedback formovies available, all Recommendations are solely based on the users’ viewing history. To overcome such limitations as well as the extra problem of user histories that are usually themerger of the preferences of all persons in each household, we have performed extensive experiments with open-source Recommendation software such as Apache Mahout and Lens-Kit, as well as with our own implementa- tions of several user-based, item-based, and content-based Recommendation algorithms. Our results indicate that our own custommulti-threaded implementation of collaborative filtering combined with a custom content-based algorithm outperforms current state-of-the-art imple- mentations of similar algorithms both in solution quality and in response time by margins exceeding 100%in terms of recall quality and 6300%in terms of running time. The hybrid nature of the ensemble allowsthe system to perform well and to overcome inherent limitations of collaborative filtering, such as various cold-start problems. AMORE has been deployed in a production environment where it has contributed to an increase in the provider’s rental profits, while at the same time offers customer retention support.

  • Implementing a commercial-strength parallel hybrid movie Recommendation Engine
    IEEE Intelligent Systems, 2014
    Co-Authors: E. Amolochitis, Ioannis T Christou, Zheng-hua Tan
    Abstract:

    AMORE is a hybrid Recommendation system that provides movie Recommendations for a major triple-play services provider in Greece. Combined with our own implementations of several user-, item-, and content-based Recommendation algorithms, AMORE significantly outperforms other state-of-the-art implementations both in solution quality and response time. AMORE currently serves daily Recommendation requests for all active subscribers of the provider's video-on-demand services and has contributed to an increase of rental profits and customer retention.