Online social trust reinforced personalized recommendation

Abstract

Recommendation techniques greatly promote the development of online service in the interconnection environment. Personalized recommendation has attracted researchers' special attention because it is more targeted to individual tasks with the characteristics of diversification and novelty. However, the data sets that personalized recommendation process usually possess the characteristics of data sparseness and information loss, which is more likely to have problems such as cognitive deviation and interest drift. To solve these issues, in recent years people gradually notice the important role in which trust factor plays in promoting the development of personalized recommendation. Given the difference between online social trust and traditional offline social trust in facilitating personalized recommendation, this paper proposes a novel technique of online social trust reinforced personal recommendation to improve the recommendation performance. Compared with traditional offline social trust-based personal recommendation, our work synthesizes both factors of online social trust and offline social trust to identify private and public trusted user communities. The trusted degree or the accredited degree can be deduced by Bayesian network probabilistic inferences. In this way, the performance of personalized recommendation can be improved by avoiding excessive interest deviation. Moreover, we also get time sequence into personal recommendation model to effectively track how user's interest changes over time. Accordingly, the recommendation accuracy can be improved by eliminating the unfavorable effect of interest drift caused by temporal variation. Empirical experiments on typical Yelp testing data set illustrate the effectiveness of the proposed recommendation technique.