Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Identifying early buyers from purchase data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Personalized recommendation driven by information flow
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A new approach to evaluating novel recommendations
Proceedings of the 2008 ACM conference on Recommender systems
The wisdom of the few: a collaborative filtering approach based on expert opinions from the web
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
ItemRank: a random-walk based scoring algorithm for recommender engines
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Ordering innovators and laggards for product categorization and recommendation
Proceedings of the third ACM conference on Recommender systems
Serendipitous recommendations via innovators
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Random walk based entity ranking on graph for multidimensional recommendation
Proceedings of the fifth ACM conference on Recommender systems
Personalized expert-based recommender system: training C-SVM for personalized expert identification
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Recommender systems are designed to predict user preference for items using his/her past activities. Predominant studies have focused on modeling and developing recommendation algorithm to predict the user preference accurately. In this paper, we assume there are some more reliable and important users for recommendation process, who have deep and broad knowledge of specific domains. Instead of developing a new recommendation model, we propose a method for quantifying user's expertise and exploiting tem to improve performance of existing recommendation algorithms. More specifically, we suggest three general expert factors called early adoption (EA), heavy access (HA) and niche-item access (NA), and we explain how to determine the expertise of each user using a latent variable model. Additionally, we show how our method can be applied to existing recommendation models. On the real-world data from last.fm, our approach shows not only accurate but novel and serendipitous recommendation.