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| A Recommendation Algorithm Based on Random Walk in Trust Network |
| Yuan Fuyong, Cai Honglei |
College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao 066004, China |
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Abstract [Objective] The current recommendation algorithm based on trust confronted with these issues: the explicit trust value is not accurate enough, the implicit trust value is hard to measure, the trust propagation path is not easy to determine. For that this paper presents a recommendation algorithm based on random walk in the trust network. [Methods] The algorithm uses the Bipartite network-based projection structure measure trust value between users, then these values are the formation of the transition probability matrix for random-walk with restart in the users projection network, random walk process does not stop until the trust distribution tends to be steady, namely the trust maximum entropy is achieved. The trust distribution at this time is the final trust matrix. [Results] The experiments on MovieLens dataset show that the improved Bipartite recommendation algorithm by adding user preferences significantly improves the Mean Absolute Error (MAE), Mean Reciprocal Rank(MRR) and normalize Discounted Cumulative Gain (nDCG) compared to other algorithms. [Limitations] Due to the cold start problems in the Bipartite network-based projection algorithm, this algorithm suffers from the new user/new item problem also. [Conclusions] That is to say, this algorithm can make the recommendation more accurate and successfully recommended objects rank in the front of the list, so this algorithm has a strong application value.
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Received: 25 February 2014
Published: 28 November 2014
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