GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Novelty and redundancy detection in adaptive filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Personalized recommendation driven by information flow
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
REFEREE: an open framework for practical testing of recommender systems using ResearchIndex
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A new approach to evaluating novel recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Personalized recommendation based on the personal innovator degree
Proceedings of the third ACM conference on Recommender systems
Trend analysis model: trend consists of temporal words, topics, and timestamps
Proceedings of the fourth ACM international conference on Web search and data mining
Predicting future reviews: sentiment analysis models for collaborative filtering
Proceedings of the fourth ACM international conference on Web search and data mining
Serendipitous recommendation for scholarly papers considering relations among researchers
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Determining user expertise for improving recommendation performance
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
A generic graph-based multidimensional recommendation framework and its implementations
Proceedings of the 21st international conference companion on World Wide Web
Recommend at opportune moments
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Serendipitous Personalized Ranking for Top-N Recommendation
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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To realize services that provide serendipity, this paper assesses the surprise of each user when presented recommendations. We propose a recommendation algorithm that focuses on the search time that, in the absence of any recommendation, each user would need to find a desirable and novel item by himself. Following the hypothesis that the degree of user's surprise is proportional to the estimated search time, we consider both innovators' preferences and trends for identifying items with long estimated search times. To predict which items the target user is likely to purchase in the near future, the candidate items, this algorithm weights each item that innovators have purchased and that reflect one or more current trends; it then lists them in order of decreasing weight. Experiments demonstrate that this algorithm outputs recommendations that offer high user/item coverage, a low Gini coefficient, and long estimated search times, and so offers a high degree of recommendation serendipitousness.