SmarterDeals: a context-aware deal recommendation system based on the smartercontext engine

Daily-deal applications are popular implementations of on-line advertising strategies that offer products and services to users based on their personal profiles. The current implementations are effective but can frustrate users with irrelevant deals due to stale profiles. To exploit these applications fully, deals must become smarter and context-aware. This paper presents SmarterDeals, our deal recommendation system that exploits users' changing personal context information to deliver highly relevant offers. SmarterDeals relies on recommendation algorithms based on collaborative filtering, and SmarterContext, our adaptive context management framework. SmarterContext provides SmarterDeals with up-to-date information about users' locations and product preferences gathered from their past and present web interactions. For many deal categories the accuracy of SmarterDeals is between 3% and 8% better than the approaches we used as baselines. For some categories, and in terms of multiplicative relative performance, SmarterDeals outperforms related approaches by as much as 173.4%, and 37.5% on average.

[1]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[2]  Nicholas J. Belkin,et al.  Information filtering and information retrieval: two sides of the same coin? , 1992, CACM.

[3]  Linas Baltrunas,et al.  Towards Time-Dependant Recommendation based on Implicit Feedback , 2009 .

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

[5]  Annie Chen,et al.  Context-aware collaborative filtering system: predicting the user's preferences in ubiquitous computing , 2005, CHI Extended Abstracts.

[6]  Joanna W. Ng,et al.  The Personal Web: smart internet for me , 2010, CASCON.

[7]  Dan Brickley,et al.  Rdf vocabulary description language 1.0 : Rdf schema , 2004 .

[8]  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.

[9]  Terrence A. Brooks,et al.  World Wide Web Consortium (W3C) , 2010 .

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

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

[12]  Jadwiga Indulska,et al.  A survey of context modelling and reasoning techniques , 2010, Pervasive Mob. Comput..

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

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

[15]  Lars Schmidt-Thieme,et al.  Fast context-aware recommendations with factorization machines , 2011, SIGIR.

[16]  Alexander Tuzhilin,et al.  Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems , 2009, RecSys '09.

[17]  Sofiane Abbar,et al.  Context-Aware Recommender Systems: A Service-Oriented Approach , 2009, VLDB 2009.

[18]  Gediminas Adomavicius,et al.  Context-aware recommender systems , 2008, RecSys '08.

[19]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[20]  Elaine Rich,et al.  User Modeling via Stereotypes , 1998, Cogn. Sci..

[21]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.

[22]  Hausi A. Müller,et al.  Managing Dynamic Context to Optimize Smart Interactions and Services , 2010, The Smart Internet.

[23]  Yehuda Koren,et al.  Advances in Collaborative Filtering , 2011, Recommender Systems Handbook.

[24]  Gediminas Adomavicius,et al.  Incorporating contextual information in recommender systems using a multidimensional approach , 2005, TOIS.

[25]  Nuria Oliver,et al.  Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering , 2010, RecSys '10.

[26]  Francesco Ricci,et al.  Context-based splitting of item ratings in collaborative filtering , 2009, RecSys '09.

[27]  Mark Claypool,et al.  Combining Content-Based and Collaborative Filters in an Online Newspaper , 1999, SIGIR 1999.

[28]  Surajit Chaudhuri,et al.  An overview of data warehousing and OLAP technology , 1997, SGMD.

[29]  Bamshad Mobasher,et al.  Contextual Recommendation , 2007, WebMine.

[30]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[31]  Hausi A. Müller,et al.  A dynamic context management infrastructure for supporting user-driven web integration in the personal web , 2011, CASCON.

[32]  Linas Baltrunas,et al.  Exploiting contextual information in recommender systems , 2008, RecSys '08.

[33]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.

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

[35]  Patrick Brézillon,et al.  Understanding Context Before Using It , 2005, CONTEXT.

[36]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[37]  Gerard Salton,et al.  Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer , 1989 .

[38]  Tim Berners-Lee,et al.  Linked Data - The Story So Far , 2009, Int. J. Semantic Web Inf. Syst..

[39]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[40]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[41]  Mark Rosenstein,et al.  Recommending and evaluating choices in a virtual community of use , 1995, CHI '95.

[42]  Euiho Suh,et al.  Context-aware systems: A literature review and classification , 2009, Expert Syst. Appl..

[43]  Yehuda Koren,et al.  The BellKor solution to the Netflix Prize , 2007 .

[44]  Hausi A. Müller,et al.  The SmarterContext Ontology and Its Application to the Smart Internet: A Smarter Commerce Case Study , 2013, The Personal Web.

[45]  L. Stein,et al.  OWL Web Ontology Language - Reference , 2004 .

[46]  Alexander Tuzhilin,et al.  Using Context to Improve Predictive Modeling of Customers in Personalization Applications , 2008, IEEE Transactions on Knowledge and Data Engineering.

[47]  Ian Horrocks,et al.  OWL Web Ontology Language Reference-W3C Recommen-dation , 2004 .

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