A Collaborative Filtering Recommender Approach by Investigating Interactions of Interest and Trust

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 214)


Collaborative filtering-based recommenders operate on the assumption that similar users share similar tastes; however, due to data sparsity of the input ratings matrix, traditional collaborative filtering methods suffer from low accuracy because of the difficulty in finding similar users and the lack of knowledge about the preference of new users. This paper proposes a recommender system based on interest and trust to provide an enhanced recommendations quality. The proposed method incorporates trust derived from both explicit and implicit feedback data to solve the problem of data sparsity. New users can highly benefit from aggregated trust and interest in the form of reputation and popularity of a user as a recommender. The performance is evaluated using two datasets of different sparsity levels, viz. Jester dataset and MovieLens dataset, and are compared with traditional collaborative filtering-based approaches for generating recommendations.


Collaborative filtering Data sparsity Cold start Interest and trust 



This work is supported in part by the National Key Technology R&D Program (No. 2012BAH16F02), the Natural Science Foundation of China (Grant No. 61003254 and No. 60903038), and the Fundamental Research Funds for the Central Universities.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyZhejiang UniversityHangzhouChina
  2. 2.College of InformationZhejiang University of Finance and EconomicsHangzhouChina

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