Leveraging Product Adopter Information from Online Reviews for Product Recommendation

The availability of the sheer volume of online product reviews makes it possible to derive implicit demographic information of product adopters from review documents. This paper proposes a novel approach to the extraction of product adopter mentions from online reviews. The extracted product adopters are the ncategorise into a number of different demographic user groups. The aggregated demographic information of many product adopters can be used to characterize both products and users, which can be incorporated into a recommendation method using weighted regularised matrix factorisation. Our experimental results on over 15 million reviews crawled from JINGDONG, the largest B2C e-commerce website in China, show the feasibility and effectiveness of our proposed frame work for product recommendation.

[1]  Xiaohui Yu,et al.  ARSA: a sentiment-aware model for predicting sales performance using blogs , 2007, SIGIR.

[2]  Marios Poulos,et al.  Using Online Consumer Reviews as a Source for Demographic Recommendations: A Case Study Using Online Travel Reviews , 2013, Expert Syst. Appl..

[3]  M GiaglisGeorge,et al.  A hybrid approach for improving predictive accuracy of collaborative filtering algorithms , 2007 .

[4]  Huan Liu,et al.  eTrust: understanding trust evolution in an online world , 2012, KDD.

[5]  William A. Cohen.  The Marketing Plan , 1994 .

[6]  Martin Ester,et al.  TrustWalker: a random walk model for combining trust-based and item-based recommendation , 2009, KDD.

[7]  Ingrid Zukerman,et al.  Personalised rating prediction for new users using latent factor models , 2011, HT '11.

[8]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

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

[10]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[11]  Izak Benbasat,et al.  A study of demographic embodiments of product recommendation agents in electronic commerce , 2010, Int. J. Hum. Comput. Stud..

[12]  Michael R. Lyu,et al.  Improving Recommender Systems by Incorporating Social Contextual Information , 2011, TOIS.

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

[14]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

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

[16]  Michael Giering,et al.  Retail sales prediction and item recommendations using customer demographics at store level , 2008, SKDD.

[17]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[18]  Amélie Marian,et al.  Improving the quality of predictions using textual information in online user reviews , 2013, Inf. Syst..

[19]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[20]  Paolo Avesani,et al.  Trust-aware recommender systems , 2007, RecSys '07.

[21]  George M. Giaglis,et al.  A hybrid approach for improving predictive accuracy of collaborative filtering algorithms , 2007, User Modeling and User-Adapted Interaction.

[22]  Amélie Marian,et al.  Beyond the Stars: Improving Rating Predictions using Review Text Content , 2009, WebDB.

[23]  Natalie S. Glance,et al.  Star Quality: Aggregating Reviews to Rank Products and Merchants , 2010, ICWSM.

[24]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .