Hybrid Trust-Driven Recommendation System for E-commerce Networks

In traditional recommendation systems, the challenging issues in adopting similarity-based approaches are sparsity, cold-start users and trustworthiness. We present a new paradigm ofrecommendation system which can utilize information from social networks including user preferences, item's generalacceptance, and influence from friends. A probabilistic model, particularly for e-commerce networks, is developed in this paperto make personalized recommendations from such information. Our analysis reveals that similar friends have a tendency to select the same items and give similar ratings. We propose atrust-driven recommendation method known as HybridTrustWalker. First, a matrix factorization method isutilized to assess the degree of trust between users. Next, an extended random walk algorithm is proposed to obtain recommendation results. Experimental results show that ourproposed system improves the prediction accuracy of recommendation systems, remedying the issues inherent incollaborative filtering to lower the user’s search effort by listing items of highest utility

کلیدواژه ها:

Recommendations system ، Trust-Driven ، Social Network ، e-commerce ، HybridTrustWalker