ABSTRACT
Recommender systems have been developed to address the abundance of choice we face in taste domains (films, music, restaurants) when shopping or going out. However, consumers currently struggle to evaluate the appropriateness of recommendations offered. With collaborative filtering, recommendations are based on people's ratings of items. In this paper, we propose that the usefulness of recommender systems can be improved by including more information about recommenders. We conducted a laboratory online experiment with 100 participants simulating a movie recommender system to determine how familiarity of the recommender, profile similarity between decision-maker and recommender, and rating overlap with a particular recommender influence the choices of decision-makers in such a context. While familiarity in this experiment did not affect the participants' choices, profile similarity and rating overlap had a significant influence. These results help us understand the decision-making processes in an online context and form the basis for user-centered social recommender system design.
- E. Berscheid & H. T. Reis. Attraction and close relationships. D. T. Gilbert, Fiske, S. T, and Linzey, G., The Handbook of Social Psychology Vol. 2, 4th{22}, Oxford University Press, (1998), p. 193--281.Google Scholar
- P. Bonhard & M. A. Sasse. I thought it was terrible and everyone else loved it"" - A New Perspective for Effective Recommender System Design. Springer Verlag, In Proc. of the 19th British HCI Group Annual Conference, Napier University, Edinburgh, UK 5-9 September 2005 (2005), p. 251--265.Google Scholar
- J. S. Breese, D. Heckerman, C. Kadie. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proc. of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence (1998), p. 43--52. Google ScholarDigital Library
- J. F. Canny. Collaborative Filtering with Privacy via Factor Analysis. In Proc. of SIGIR 2002, Tampere, Finland (2002), p. 238--245. Google ScholarDigital Library
- D. Cosley, P. J. Ludford, L. Terveen. Studying the effect of similarity in online task-focused interactions. ACM Press, In Proc. of the 2003 international ACM SIGGROUP conference on Supporting group work (2003), p. 321--329. Google ScholarDigital Library
- U. Flick. An Introduction to Qualitative Research. London, Sage Publications, (1998).Google Scholar
- G. Gigerenzer. Fast and Frugal Heuristics: The Tools of Bounded Rationality. N. Harvey & Koehler, Derek, Blackwell Handbook of Judgment and Decision Making, {4}Oxford, Blackwell, (2004), p. 62--88.Google Scholar
- N. Harvey & I. Fischer. Taking Advice: Accepting Help, Improving Judgement, and Sharing Responsibility. In Organizational Behavior and Human Decision Processes 70{2}(1997), p. 117--133.Google Scholar
- J. L. Herlocker, J. A. Konstan, A. Borchers, J. Riedl. An Algorithmic Framework for Performing Collaborative Filtering. In Proc. of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (1999), p. 230--237. Google ScholarDigital Library
- J. L. Herlocker, J. A. Konstan, A. Borchers, J. Riedl. Explaining Collaborative Filtering Recommendations. In Proc. of the ACM 2000 Conference on Computer Supported Cooperative Work (2000), p. 241--250. Google ScholarDigital Library
- P. Massa & P. Avesani. Trust-aware Collaborative Filtering for Recommender Systems. In Proc. of The International Conference on Cooperative Information Systems (CoopIS), 25 - 29 October 2004, Larnaca, Cyprus (2004).Google Scholar
- M. R. McLaughlin & J. L. Herlocker. A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In Proc. of the 27th annual international ACM SIGIR conference on research and development in information retrieval (2004), p. 329--336. Google ScholarDigital Library
- J. O'Donovan & B. Smyth. Trust in Recommender Systems. In Proc. of The International Conference on Intelligent User Interfaces, San Diego, California, Jan 9-12, 2005 (2005), p. 167--174. Google ScholarDigital Library
- J. W. Payne, J. R. Bettman, M. F. Luce. Behavioural Decision Research: An Overview. Measurement, Judgement and Decision Making, {5}London, Academic Press, (1998), p. 303--359.Google Scholar
- S. Perugini, M. A. Goncalves, E. A. Fox. Recommender Systems Research: A Connection-Centric Survey. In Journal of Intelligent Information Systems 23{2}(2004), p. 107--143. Google ScholarDigital Library
- P. Resnick & H. R. Varian. Recommender Systems. In Communications of the ACM 40{3}(1997), p. 56--58. Google ScholarDigital Library
- B. M. Sarwar, G. Karypis, J. A. Konstan, J. Riedl. Analysis of Recommendation Algorithms for E-Commerce. In Proc. of the ACM E-Commerce '00 Conference. Minneapolis, MN (2000), p. 158--167. Google ScholarDigital Library
- B. M. Sarwar, G. Karypis, J. A. Konstan, J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proc. of the Tenth International World Wide Web Conference (2001), p. 285--295. Google ScholarDigital Library
- B. Schwartz. The Paradox of Choice. Harper Collins, (2005)Google Scholar
- K. Swearingen & R. Sinha. Beyond algorithms: An HCI perspective on recommender systems. In Proc. of ACM SIGIR 2001 Workshop on Recommender Systems, New Orleans, Lousiana (2001), p. 24--33.Google Scholar
- K. Swearingen & R. Sinha. Interaction design for recommender systems. http://www.rashmisinha.com/articles/musicDIS.pdf (last accessed 2006-1-15).Google Scholar
- I. Yaniv. Receiving other peoples' advice: Influence and Benefit. In Organizational Behavior and Human Decision Processes 93{1}(2004), p. 1--13.Google Scholar
- I. Yaniv. The Benefit of Additional Opinions. In American Psychological Society 13{2}(2004), p. 75--78.Google Scholar
- I. Yaniv & E. Kleinberger. Advice Taking in Decision Making: Egocentric Discounting and Reputation Formation. In Organizational Behavior and Human Decision Processes 83{2}(2000), p. 260--281.Google Scholar
Index Terms
- Accounting for taste: using profile similarity to improve recommender systems
Recommendations
"The devil you know knows best": how online recommendations can benefit from social networking
BCS-HCI '07: Proceedings of the 21st British HCI Group Annual Conference on People and Computers: HCI...but not as we know it - Volume 1The defining characteristic of the Internet today is an abundance of information and choice. Recommender Systems (RS), designed to alleviate this problem, have so far not been very successful, and recent research suggests that this is due to the lack of ...
Social group recommendation in the tourism domain
Recommender Systems learn users' preferences and tastes in different domains to suggest potentially interesting items to users. Group Recommender Systems generate recommendations that intend to satisfy a group of users as a whole, instead of individual ...
Double-sided recommendations: a novel framework for recommender systems
AI*IA'11: Proceedings of the 12th international conference on Artificial intelligence around man and beyondRecommender systems actively provide users with suggestions of potentially relevant items. In this paper we introduce double-sided recommendations, i.e., recommendations consisting of an item and a group of people with whom such an item could be ...
Comments