Abstract
Personalized search utilizes user preferences to optimize search results, and most existing studies obtain user preferences by analyzing user behaviors in search engines that provide click-through data. However, the behavioral data are noisy because users often clicked some irrelevant documents to find their required information, and the new user cold start issue represents a serious problem, greatly reducing the performance of personalized search. This paper attempts to utilize online social network data to obtain user preferences that can be used to personalize search results, mine the knowledge of user interests, user influence and user relationships from online social networks, and use this knowledge to optimize the results returned by search engines. The proposed model is based on a holonic multiagent system that improves the adaptability and scalability of the model. The experimental results show that utilizing online social network data to implement personalized search is feasible and that online social network data are significant for personalized search.
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