Bradley N. Miller
Istvan Albert
Joseph A. Konstan
John Riedl
ABSTRACT
Recommender systems have changed the way people shop online. Recommender systems on wireless mobile devices may have the same impact on the way people shop in stores. We present our experience with implementing a recommender system on a PDA that is occasionally connected to the network. This interface helps users of the MovieLens movie recommendation service select movies to rent, buy, or see while away from their computer. The results of a nine month field study show that although there are several challenges to overcome, mobile recommender systems have the potential to provide value to their users today
- Marko Balabanovic and Y:oav Shoham. Fab: Content based, collaborative recommendation as classification. Communications of the ACM, 1997. Google Scholar
Digital Library
- John S. Breese, David Heckerman, and Carl Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI-98), pages 43--52, July 1998. Google ScholarDigital Library
- George Buchanan, Sarah Farrant, Matt Jones, Harold W. Thimbleby, Gary Marsden, and Michael J. Pazzani. Improving mobile internet usability. In Proceedings of the tenth international world wide web conference, pages 673--680, 2001. Google ScholarDigital Library
- M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin. Combining content-based and collaborative filters in an online newspaper. In Proceedings of ACM SIGIR Workshop on Recommender, August 1999.Google Scholar
- Rebecca Grinter and Margery Eldridge. y do tngrs luv 2 txt msg? In Proceedings of the Seventh European Conference on Computer Supported Cooperative Work ECSCW 01. Kluwer Academic Publishers, 2001. Google ScholarDigital Library
- Jon Herlocker, Joseph Konstan, Al Borchers, and John Riedl. An algorithmic framework for performing collaborative filtering. In Proceedings of the 1999 Conference on Research and Development in Information Retrieval (SIGIR- 99), August 1999. Google ScholarDigital Library
- Joseph Konstan, Brad Miller, David Maltz, Jon Herlocker, Lee Gordon, and John Riedl. Grouplens: Collaborative filtering for usenet news. Communications of the ACM, March 1997. Special Issue on Recommendation Systems. Google ScholarDigital Library
- Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan. Content- boosted collaborative filtering for improved recommendations. In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02), July 2002. Google ScholarDigital Library
- Marc Ramsay and Jakob Nielsen. Wap usability deja vu: 1994 all over again. Technical report, Neilsen Norman Group, 2000.Google Scholar
- B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl. Analysis of recommender algorithms for e- commerce. In ACM E-Commerce 2000, 2000. Google ScholarDigital Library
- J. Ben Schafer, Joseph Konstan, and John Riedl. Recommender systems in e-commerce. In Proceedings of the ACM Conference on Electronic Commerce (EC 99), 1999. Google ScholarDigital Library
- U. Shardanand and P. Maes. Social information filtering: Algorithms for automating "word of mouth". In Human Factors in Computing Systems CHI 95 Conference Proceedings, pages 210--217, 1995. Google ScholarDigital Library
- Kirsten Swearingen and Sinha Rashmi. Interaction design for recommender systems. In Designing Interactive Systems 2002. ACM, 2002.Google Scholar
Index Terms
- MovieLens unplugged: experiences with an occasionally connected recommender system
Recommendations
A Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data MiningRecommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...
A framework for diversifying recommendation lists by user interest expansion
Recommender systems have been widely used to discover users' preferences and recommend interesting items to users during this age of information overload. Researchers in the field of recommender systems have realized that the quality of a top-N ...
Naïve filterbots for robust cold-start recommendations
KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data miningThe goal of a recommender system is to suggest items of interest to a user based on historical behavior of a community of users. Given detailed enough history, item-based collaborative filtering (CF) often performs as well or better than almost any ...
Comments