Social Commerce Hybrid Product Recommender

vision for Web 3.0 (also known as Semantic Web) is the ability to create meaning out of huge quantity of qualitative data. Existing data can be interconnected for further uses. Web 2.0 focused on the users interaction with others whereas Web 3.0 focus more on the users themselves. The advantages of Semantic Web and E-commerce give rise to social commerce (also referred as f-commerce). The future of business lies on the "social" factor and it is this factor which gives rise to a new kind of connected consumers who are becoming influential in their own right. This paper explores a very specific instance of Semantic Web - Social Recommender System. This paper discusses the likelihood of converting social data into quantitative information and using this information to power social recommendations. This paper first outlines the benefits of social commerce over e- commerce platform. Then the related literature work regarding hybrid recommenders is discussed. Next it is discussed how to predict ratings from a user-item rating network and friend's network and then how to unify similarity matrices obtained from different networks. And lastly this paper covers the social hybrid product recommender algorithm and its experimental evaluations to predict its efficiency. Keywordscommerce, hybrid product recommender, f-commerce, homophily, Cosine similarity, Smith Waterman string similarity measure, unipartite graph, bipartite graph

[1]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[2]  J. Golbeck.  Personalizing Applications through Integration of Inferred Trust Values in Semantic Web-Based Social Networks , 2005 .

[3]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[4]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[5]  David M. Pennock,et al.  Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments , 2001, UAI.

[6]  M S Waterman,et al.  Identification of common molecular subsequences. , 1981, Journal of molecular biology.

[7]  George Karypis,et al.  Evaluation of Item-Based Top-N Recommendation Algorithms , 2001, CIKM '01.

[8]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[9]  Paolo Avesani,et al.  Trust-Aware Collaborative Filtering for Recommender Systems , 2004, CoopIS/DOA/ODBASE.

[10]  Panagiotis Symeonidis,et al.  Product recommendation and rating prediction based on multi-modal social networks , 2011, RecSys '11.

[11]  Michael J. Pazzani,et al.  A hybrid user model for news story classification , 1999 .

[12]  Martin Ester,et al.  A matrix factorization technique with trust propagation for recommendation in social networks , 2010, RecSys '10.

[13]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[14]  Raymond J. Mooney,et al.  Content-boosted collaborative filtering for improved recommendations , 2002, AAAI/IAAI.

[15]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .

[16]  William W. Cohen,et al.  Recommendation as Classification: Using Social and Content-Based Information in Recommendation , 1998, AAAI/IAAI.

[17]  Michael J. Pazzani,et al.  User Modeling for Adaptive News Access , 2000, User Modeling and User-Adapted Interaction.

[18]  Wesley W. Chu,et al.  A Social Network-Based Recommender System (SNRS) , 2010, Data Mining for Social Network Data.

[19]  Robin Cohen,et al.  Hybrid Recommender Systems for Electronic Commerce , 2000 .

[20]  William E. Winkler,et al.  String Comparator Metrics and Enhanced Decision Rules in the Fellegi-Sunter Model of Record Linkage. , 1990 .

[21]  Ian Soboroff. Charles Nicholas.  Combining Content and Collaboration in Text Filtering , 1999 .

[22]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[23]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.