Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Beyond accuracy: evaluating recommender systems by coverage and serendipity
Proceedings of the fourth ACM conference on Recommender systems
Implementing an image search system with integrating social tags and DBpedia
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
Collaborative filtering for predicting users' potential preferences
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
Auralist: introducing serendipity into music recommendation
Proceedings of the fifth ACM international conference on Web search and data mining
ACM Transactions on Interactive Intelligent Systems (TiiS)
Leveraging linked data analysis for semantic recommender systems
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
User-centric evaluation of a K-furthest neighbor collaborative filtering recommender algorithm
Proceedings of the 2013 conference on Computer supported cooperative work
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
On a serendipity-oriented recommender system based on folksonomy
Artificial Life and Robotics
Modeling and broadening temporal user interest in personalized news recommendation
Expert Systems with Applications: An International Journal
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In this paper we propose metrics unexpectedness and unexpectedness_r for measuring the serendipity of recommendation lists produced by recommender systems. Recommender systems have been evaluated in many ways. Although prediction quality is frequently measured by various accuracy metrics, recommender systems must be not only accurate but also useful. A few researchers have argued that the bottom-line measure of the success of a recommender system should be user satisfaction. The basic idea of our metrics is that unexpectedness is the distance between the results produced by the method to be evaluated and those produced by a primitive prediction method. Here, unexpectedness is a metric for a whole recommendation list, while unexpectedness_r is that taking into account the ranking in the list. From the viewpoints of both accuracy and serendipity, we evaluated the results obtained by three prediction methods in experimental studies on television program recommendations.