شماره ركورد كنفرانس :
3296
عنوان مقاله :
A trust-aware group recommender system using particle swarm optimization
عنوان به زبان ديگر :
A trust-aware group recommender system using particle swarm optimization
پديدآورندگان :
Sadat Gohari Faezeh Faculty of Computer Science and Engineering Shahid Beheshti University G.C. Tehran - Iran , Shams Aliee Fereidoon Faculty of Computer Science and Engineering Shahid Beheshti University G.C. Tehran - Iran , Haghighi Hassan Faculty of Computer Science and Engineering Shahid Beheshti University G.C. Tehran - Iran
كليدواژه :
collaborative filtering , particle swarm optimization , trust , group recommendation , recommender systems , component
عنوان كنفرانس :
هجدهمين سمپوزيوم بين المللي علوم كامپيوتر و مهندسي نرم افزار
چكيده لاتين :
With the exponential growth of the online community
activities, group recommender systems have become popular in
recent years. However, making recommendations relevant to the
common interests of a group is a challenging task due to the
diversity of group members’ preferences. In this paper, we
propose a novel Trust-aware Group Recommendation (TGR)
approach to improve the performance of group
recommendations. TGR uses a new group trust metric that is
optimized by Particle Swarm Optimization (PSO). This metric
directly provides a set of neighbors for a group of users. The
experimental results show that TGR can improve the accuracy
and run-time performance of other group recommender systems.
