Does the Users' Tendency to Seek Information Affect Recommender Systems' Performance?

Much work has been done on developing recommender system (RS) algorithms, on comparing them using business metrics (such as customers’ trust or perception of recommendations’ novelty) and on exploring users’ reactions to recommendations. It was demonstrated that different recommender systems perform differently on several performance metrics and that different users react differently to the same kind of recommendations. As a consequence, some scholars challenged to explore how users with different tendency to seek information during their purchasing process may react to different kind of recommendations. To the best of our knowledge, none of the prior works studied if users’ tendency to seek information has an effect on recommender systems’ performance. Different users may traditionally have different propensity to seek information and to receive suggestions and therefore they may react differently to the same recommendations. To this aim, we performed a live experiment with real customers coming from a European firm.

[1]  Alexander Tuzhilin,et al.  The effect of context-aware recommendations on customer purchasing behavior and trust , 2011, RecSys '11.

[2]  Li Chen,et al.  A user-centric evaluation framework for recommender systems , 2011, RecSys '11.

[3]  Michael J. Davern,et al.  Personalizing to product category knowledge: exploring the mediating effect of shopping tools on decision confidence , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[4]  Xiaodong He,et al.  A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems , 2015, WWW.

[5]  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.

[6]  Rong Hu,et al.  Acceptance issues of personality-based recommender systems , 2009, RecSys '09.

[7]  Barton A. Weitz,et al.  Marketing the Unfamiliar: The Role of Context and Item-Specific Information in Electronic Agent Recommendations , 2002 .

[8]  F. Maxwell Harper,et al.  Letting Users Choose Recommender Algorithms: An Experimental Study , 2015, RecSys.

[9]  Abhijit Chaudhury,et al.  An empirical investigation of decision-making satisfaction in web-based decision support systems , 2004, Decis. Support Syst..

[10]  Tor Guimaraes,et al.  Assessing the impact of internet agent on end users' performance , 2005, Decis. Support Syst..

[11]  Robert E. Widing,et al.  Are interactive decision aids better than passive decision aids? A comparison with implications for information providers on the internet , 2002 .

[12]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[13]  Gerald Häubl,et al.  "Double Agents": Assessing the Role of Electronic Product Recommendation Systems , 2005 .

[14]  Yi-Cheng Ku,et al.  Personalized Content Recommendation and User Satisfaction: Theoretical Synthesis and Empirical Findings , 2006, J. Manag. Inf. Syst..

[15]  Peng Jiang,et al.  Life-stage Prediction for Product Recommendation in E-commerce , 2015, KDD.

[16]  Bart P. Knijnenburg,et al.  Understanding the effect of adaptive preference elicitation methods on user satisfaction of a recommender system , 2009, RecSys '09.

[17]  Alfred Kobsa,et al.  A pragmatic procedure to support the user-centric evaluation of recommender systems , 2011, RecSys '11.

[18]  Wendy E. Mackay,et al.  CHI '13 Extended Abstracts on Human Factors in Computing Systems , 2013, CHI 2013.

[19]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[20]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.

[21]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[22]  Jihoon Kim,et al.  Concept lattices for visualizing and generating user profiles for context-aware service recommendations , 2009, Expert Syst. Appl..

[23]  Valerie J. Trifts,et al.  Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids , 2000 .

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

[25]  Izak Benbasat,et al.  E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact , 2007, MIS Q..

[26]  Michele Gorgoglione,et al.  Comparing Pre-filtering and Post-filtering Approach in a Collaborative Contextual Recommender System: An Application to E-Commerce , 2009, EC-Web.

[27]  CARLOS A. GOMEZ-URIBE,et al.  The Netflix Recommender System , 2015, ACM Trans. Manag. Inf. Syst..

[28]  Izak Benbasat,et al.  Trust In and Adoption of Online Recommendation Agents , 2005, J. Assoc. Inf. Syst..

[29]  Kirsten Swearingen,et al.  Beyond Algorithms: An HCI Perspective on Recommender Systems , 2001 .

[30]  Joseph M. Jones,et al.  Do Internet Shopping Aids Make a Difference? An Empirical Investigation , 2001, Electron. Mark..

[31]  Ardion Beldad,et al.  How shall I trust the faceless and the intangible? A literature review on the antecedents of online trust , 2010, Comput. Hum. Behav..

[32]  Kartik Hosanagar,et al.  Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity , 2007, Manag. Sci..

[33]  J. H. Davis,et al.  An Integrative Model of Organizational Trust: Past, Present, and Future , 2007 .

[34]  Izak Benbasat,et al.  The Effects of Personalizaion and Familiarity on Trust and Adoption of Recommendation Agents , 2006, MIS Q..

[35]  Bart P. Knijnenburg,et al.  Explaining the user experience of recommender systems , 2012, User Modeling and User-Adapted Interaction.

[36]  Anthony M. Townsend,et al.  Understanding the moderating roles of types of recommender systems and products on customer behavioral intention to use recommender systems , 2014, Information Systems and e-Business Management.

[37]  Per E. Pedersen Behavioral Effects of Using Software Agents for Product and Merchant Brokering: An Experimental Study of Consumer Decision-Making , 2000, Int. J. Electron. Commer..

[38]  Seungmin Rho,et al.  Key factors affecting user experience of mobile recommendation systems , 2015, IMECS 2015.

[39]  TuzhilinAlexander,et al.  Comparing context-aware recommender systems in terms of accuracy and diversity , 2014 .

[40]  Bart P. Knijnenburg,et al.  The effect of preference elicitation methods on the user experience of a recommender system , 2010, CHI EA '10.

[41]  Rex Eugene Pereira,et al.  Influence of Query-Based Decision Aids on Consumer Decision Making in Electronic Commerce , 2001, Inf. Resour. Manag. J..

[42]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[43]  Rong Hu,et al.  A comparative user study on rating vs. personality quiz based preference elicitation methods , 2009, IUI.

[44]  Sean M. McNee,et al.  Improving recommendation lists through topic diversification , 2005, WWW '05.

[45]  Li Chen,et al.  Experiments on the preference-based organization interface in recommender systems , 2010, TCHI.

[46]  Bidyut Kr. Patra,et al.  A knowledge reuse framework for improving novelty and diversity in recommendations , 2015, CODS.

[47]  Yoon C. Cho,et al.  Exploring Factors That Affect Usefulness, Ease Of Use, Trust, And Purchase Intention In The Online Environment , 2015 .

[48]  J. H. Davis,et al.  An Integrative Model Of Organizational Trust , 1995 .

[49]  Maria Madlberger,et al.  Consumers' Interest in Personalized Recommendations: The Role of Product-Involvement and Opinion Seeking , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).

[50]  Mamta Bhusry,et al.  Recommendation System: State of the Art Approach , 2015 .

[51]  BenbasatIzak,et al.  The effects of personalizaion and familiarity on trust and adoption of recommendation agents , 2006 .

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

[53]  John Riedl,et al.  Recommender systems in e-commerce , 1999, EC '99.

[54]  John Riedl,et al.  Recommender systems: from algorithms to user experience , 2012, User Modeling and User-Adapted Interaction.

[55]  Joseph P. Cannon,et al.  Understanding the Influence of National Culture on the Development of Trust , 1998 .

[56]  Alexander Tuzhilin,et al.  Research Note - In CARSs We Trust: How Context-Aware Recommendations Affect Customers' Trust and Other Business Performance Measures of Recommender Systems , 2016, Inf. Syst. Res..

[57]  Mark P. Graus,et al.  Understanding choice overload in recommender systems , 2010, RecSys '10.

[58]  Alfred Kobsa,et al.  Impacts of User Privacy Preferences on Personalized Systems , 2004, Designing Personalized User Experiences in eCommerce.

[59]  Robert S. Moore,et al.  An Investigation of Agent Assisted Consumer Information Search: Are Consumers Better Off? , 2001 .

[60]  Ram D. Gopal,et al.  Empirical Analysis of the Impact of Recommender Systems on Sales , 2010, J. Manag. Inf. Syst..

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

[62]  Li Chen,et al.  Evaluating recommender systems from the user’s perspective: survey of the state of the art , 2012, User Modeling and User-Adapted Interaction.