科研之友
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摘要
Recently, trust models have contributed much to the success of online multimedia recommendation service. However, most of them only consider the case of binary ratings and ignore the attributes of ratings, which will limit their universal applicability. To address this problem, we propose a multi-attribute rating based trust model to improve the Zhang's Personalized trust modeling framework, an existing framework for trust modeling by using binary ratings in multi-agent electronic marketplaces. In our approach, it does not restrict users to using a single attribute rating; it allows a rating to be a certain value between 0 and 1 rather than only 0 or 1; it can improve assessment accuracy by calculating the similarity of common ratings between recommenders and users; and it considers the certainty of ratings to deal with the sudden change of partner's behaviours. Finally, experimental results show that, our approach can effectively model the trustworthiness of recommenders and providers, and it can also resist several malicious attacks.
