Workshop on Recommender Systems: Algorithms and Evaluation Ian Soboroff, Charles Nicholas, and Michael Pazzani Introduction The world of recommender systems has undergone quite an expansion since the Communications o f the A C M published their feature issue on the topic two years ago. Projects such as GroupLens have gone on to be successful commercial ventures, and recommendation systems are de rigeur for Internet commerce. The basic technology has very quickly moved from the research world to popular applications. Several methods for implementing recommender systems have emerged, including approaches that base recommendations on correlations of groups of users and methods that learn about individual users. However, the architectural issues of cold-start, sparse ratings, and scalability continue to dominate the field. The state of the art in recommender systems will be enhanced by the development of evaluation methodologies for recommender systems. User studies are difficult to conduct and generalize from, and issues of presentation and relevance make traditional IR evaluation measures not entirely suited to the domain. Furthermore, test collections such as DEC SRC's EachMovie data set are becoming standard tools, but the need for larger collections in different domains is great. Thus, the goal of the workshop was
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Workshop on recommender systems: algorithms and evaluation
Soboroff, Ian; Nicholas, Charles; Pazzani, Michael
Follow ACM SIGIR Forum
, Volume 33 (1)
Association for Computing Machinery – Sep 1, 1999
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