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Trust recommender systems can depend on users' previous opinions and other trustworthy users' opinions on items suggest to them items they will like. However, it is very important to discern and find trustworthy users' opinions. In fact, it is easier for experts to find higher quality Web pages safely in search engines and trustworthy search histories are produced at the same time. Based on this observation, we firstly introduce a Trust Recommender Model (TRM) in this paper and it can recommend trustworthy Web pages to users through utilizing experts and their search histories based on experts' social networks. Then, we propose an approach to identify right experts for a given queries and a fast traversing experts' social network algorithm, which can be used to recommend trustworthy experts' search histories in real time. In addition, in order to illuminate and validate proposed methods and algorithm, a Web pages recommender system is implemented through extending the open source search engine “Nutch”.