科研成果详情

Proliferation of Electronic Commerce (EC) has revolutionized the way people purchase online. Web-based technologies enable people to more actively interact with merchants and service providers. Such purchasing logs and comments further lead to proliferation of recommender systems. Existing recommendation algorithms exploit either prior transactions or customer reviews to predict user interests towards certain items. Vast noise may be introduced into such information by fake raters, and information redundancy also makes recommender system entangled. In this work, we first examine user reviews and prior transactions to estimate user credibility and item importance to reduce effect from content polluters. Then we propose to alleviate the redundant information from homogeneous users based on link analysis. A unified framework is finally proposed to incorporate them in a mathematical formulation, which can be efficiently optimized. Experimental results on real world data reveal that our model can significantly outperform other baselines.