GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Pointing the way: active collaborative filtering
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Spreading Activation Models for Trust Propagation
EEE '04 Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'04)
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Using probabilistic confidence models for trust inference in Web-based social networks
ACM Transactions on Internet Technology (TOIT)
Hybrid web recommender systems
The adaptive web
Item popularity and recommendation accuracy
Proceedings of the fifth ACM conference on Recommender systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Recommender systems (RS) are designed to assist users by recommending them items they should appreciate. User based RS exploits users behavior to generate recommendations. Users act in accordance with different modes when using RS, so RS's performance fluctuates across users, depending on their act mode. Act here includes quantitative and qualitative features of user behavior. When RS is applied in an e-commerce dedicated social network, these features include but are not limited to: user's number of ratings, user's number of friends, the items he chooses to rate, the value of his ratings, and the reputation of his friends. This set of features can be considered as the user's profile. In this work, we cluster users according to their acting profiles, then we compare the performance of three different recommenders on each cluster, to explain RS's performance fluctuation across different users' acting modes.