Trust plays an important role in helping social network user make appropriate decision from abundant social information about products. A variety of social review information (e.g. ratings, voting and tags) emerge with the huge number of products on the Web. How they are utilized for searching and finding appropriate item is investigated. In this paper we propose a user credibility calculation method, which calculates user's credibility through his previous review information. By evaluating facets of every review, an integrated numerical value which denotes the reviewer's credibility can be calculated. This value can be used to rank products, further to help user making appropriate decision. Experiments on social book search database show that this calculation method improves effectively accuracy of recommended items.

During every summer holidays, several editions of reading lists are recommended and emerged on mass media, e.g., New York Times, and BBC. However, these reading lists are built for whole people with general topics for some purposes. What if we expect the books of a specific topic at a specific moment? How to generate the requested reading list for our own automatically? In this paper, we propose a searching framework for building a topical reading list anytime, where the Relevance (between topics and books), Quality (of books), Timeliness (of popularities) and Diversity (of results) are embedded into vector representations respectively based on user-generated contents and statistics on social media. We collected 8,197 real-world topics from 198 diverse groups on Librarything.com. The proposed methods are evaluated on the topic collection and the public benchmarks Social Book Search 2012-2016 (SBS). Experimental results demonstrate the robustness and effectiveness of our framework.