A Framework for Modeling, Computing and Presenting Time-Aware Recommendations

Lately, recommendation systems have received significant attention. Most existing approaches though, recommend items of potential interest to users by completely ignoring the temporal aspects of ratings. In this paper, we argue that time-aware recommendations need to be pushed in the foreground. We introduce an extensive model for time-aware recommendations from two perspectives. From a fresh-based perspective, we propose using different aging schemes for decreasing the effect of historical ratings and increasing the influence of fresh and novel ratings. From a context-based perspective, we focus on providing different suggestions under different temporal specifications. To facilitate user browsing, we propose an effective presentation layer for time-aware recommendations based on user preferences and summaries for the suggested items. Our experiments with real movies ratings show that time plays an important role in the recommendation process.

[1]  Georgios John Fakas.  A novel keyword search paradigm in relational databases: Object summaries , 2011, Data Knowl. Eng..

[2]  Nong Ye,et al.  The Handbook of Data Mining , 2003 .

[3]  Letizia Tanca,et al.  A methodology for preference-based personalization of contextual data , 2009, EDBT '09.

[4]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[5]  M. de Rijke,et al.  Broad expertise retrieval in sparse data environments , 2007, SIGIR.

[6]  Georgia Koutrika,et al.  Personalizing queries based on networks of composite preferences , 2010, TODS.

[7]  Zhi Cai,et al.  Size-l Object Summaries for Relational Keyword Search , 2011, Proc. VLDB Endow..

[8]  Aoying Zhou,et al.  Density-Based Clustering over an Evolving Data Stream with Noise , 2006, SDM.

[9]  Georgia Koutrika,et al.  Data clouds: summarizing keyword search results over structured data , 2009, EDBT '09.

[10]  Benjamin M. Good,et al.  Tag clouds for summarizing web search results , 2007, WWW '07.

[11]  Werner Kießling,et al.  Foundations of Preferences in Database Systems , 2002, VLDB.

[12]  John Riedl,et al.  PolyLens: A recommender system for groups of user , 2001, ECSCW.

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

[14]  Qing Yang,et al.  Time-Dependent Models in Collaborative Filtering Based Recommender System , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

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

[16]  Jaideep Srivastava,et al.  Automatic personalization based on Web usage mining , 2000, CACM.

[17]  Gediminas Adomavicius,et al.  New Recommendation Techniques for Multicriteria Rating Systems , 2007, IEEE Intelligent Systems.

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

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

[20]  Yoav Shoham,et al.  Content-Based, Collaborative Recommendation. , 1997 .

[21]  Evaggelia Pitoura,et al.  Managing contextual preferences , 2011, Inf. Syst..

[22]  Charu C. Aggarwal,et al.  Data Streams - Models and Algorithms , 2014, Advances in Database Systems.

[23]  Cong Yu,et al.  Group Recommendation: Semantics and Efficiency , 2009, Proc. VLDB Endow..

[24]  Jimeng Sun,et al.  Temporal recommendation on graphs via long- and short-term preference fusion , 2010, KDD.

[25]  Michael J. Pazzani,et al.  Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.

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

[27]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[28]  Bracha Shapira,et al.  Recommender Systems Handbook , 2015, Springer US.

[29]  Hans-Peter Kriegel,et al.  Fast Group Recommendations by Applying User Clustering , 2012, ER.

[30]  Rakesh Agrawal,et al.  A framework for expressing and combining preferences , 2000, SIGMOD '00.

[31]  Xue Li,et al.  Time weight collaborative filtering , 2005, CIKM '05.

[32]  Philip S. Yu,et al.  A Framework for Clustering Evolving Data Streams , 2003, VLDB.

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

[34]  Jan Chomicki,et al.  Preference formulas in relational queries , 2003, TODS.

[35]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[36]  Umeshwar Dayal,et al.  A Uniform Approach to Processing Temporal Queries , 1992, VLDB.

[37]  Nicolas Spyratos,et al.  Efficient Rewriting Algorithms for Preference Queries , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[38]  Jesús S. Aguilar-Ruiz,et al.  Knowledge discovery from data streams , 2009, Intell. Data Anal..