Emotions in Context-Aware Recommender Systems

Recommender systems are decision aids that offer users personalized suggestions for products and other items. Context-aware recommender systems are an important subclass of recommender systems that take into account the context in which an item will be consumed or experienced. In context-aware recommendation research, a number of contextual features have been identified as important in different recommendation applications: such as companion in the movie domain, time and mood in the music domain, and weather or season in the travel domain. Emotions have also been demonstrated to be significant contextual factors in a variety of recommendation scenarios. In this chapter, we describe the role of emotions in context-aware recommendation, including defining and acquiring emotional features for recommendation purposes, incorporating such features into recommendation algorithms. We conclude with a sample evaluation , showing the utility of emotion in recommendation generation.

[1]  Rosalind W. Picard.  Affective Computing , 1997 .

[2]  Robin Burke,et al.  Differential Context Modeling in Collaborative Filtering , 2013 .

[3]  Yong Zheng,et al.  Context Suggestion: Solutions and Challenges , 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).

[4]  K. Scherer.  What are emotions? And how can they be measured? , 2005 .

[5]  Bamshad Mobasher,et al.  Integrating Context Similarity with Sparse Linear Recommendation Model , 2015, UMAP.

[6]  Francesco Ricci,et al.  Context-Aware Recommender Systems , 2011, AI Mag..

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

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

[9]  Judith Masthoff,et al.  The Pursuit of Satisfaction: Affective State in Group Recommender Systems , 2005, User Modeling.

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

[11]  Bernd Ludwig,et al.  Matrix factorization techniques for context aware recommendation , 2011, RecSys '11.

[12]  Rafael A. Calvo,et al.  Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications , 2010, IEEE Transactions on Affective Computing.

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

[14]  Ante Odic,et al.  The impact of weak ground truth and facial expressiveness on affect detection accuracy from time-continuous videos of facial expressions , 2013, Inf. Sci..

[15]  Bamshad Mobasher,et al.  Splitting approaches for context-aware recommendation: an empirical study , 2014, SAC.

[16]  D. Kahneman,et al.  Heuristics and Biases: The Psychology of Intuitive Judgment , 2002 .

[17]  Francesco Ricci,et al.  Experimental evaluation of context-dependent collaborative filtering using item splitting , 2013, User Modeling and User-Adapted Interaction.

[18]  Kasia Muldner,et al.  Emotion Sensors Go To School , 2009, AIED.

[19]  Bamshad Mobasher,et al.  CSLIM: contextual SLIM recommendation algorithms , 2014, RecSys '14.

[20]  Robin Burke,et al.  Optimal Feature Selection for Context-Aware Recommendation using Differential Relaxation , 2012 .

[21]  George N. Votsis,et al.  Emotion recognition in human-computer interaction , 2001, IEEE Signal Process. Mag..

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

[23]  Marko Tkalcic,et al.  Affective recommender systems: The role of emotions in recommender systems , 2011 .

[24]  Bamshad Mobasher,et al.  Recommendation with Differential Context Weighting , 2013, UMAP.

[25]  Bamshad Mobasher,et al.  Similarity-Based Context-Aware Recommendation , 2015, WISE.

[26]  Bamshad Mobasher,et al.  Deviation-Based Contextual SLIM Recommenders , 2014, CIKM.

[27]  Bamshad Mobasher,et al.  Differential Context Relaxation for Context-Aware Travel Recommendation , 2012, EC-Web.

[28]  Bamshad Mobasher,et al.  Context Recommendation Using Multi-label Classification , 2014, 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[29]  Songbo Tan,et al.  A survey on sentiment detection of reviews , 2009, Expert Syst. Appl..

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

[31]  Bamshad Mobasher,et al.  CARSKit: A Java-Based Context-Aware Recommendation Engine , 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).

[32]  Yong Zheng,et al.  A Revisit to The Identification of Contexts in Recommender Systems , 2015, IUI Companion.

[33]  Jurij F. Tasic,et al.  Predicting and Detecting the Relevant Contextual Information in a Movie-Recommender System , 2013, Interact. Comput..

[34]  Sahin Albayrak,et al.  Inferring Contextual User Profiles - Improving Recommender Performance , 2011 .

[35]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.