Recommender systems help customers to choose right product or service from large number of alternatives available on Internet. In recent time, trust becomes an important issue in designing effective recommender systems. In this paper we have studied the role of trust and distrust in designing recommender systems. General Terms E-Commerce, Information Retrieval, Web Mining.

With the continuous growth of the Internet and the progress of electronic commerce the issues of product recommendation and privacy protection are becoming increasingly important. Recommender Systems aim to solve the information overload problem by providing accurate recommendations of items to users. Collaborative filtering is considered the most widely used recommendation method for providing...

Collaborative filtering (CF) is widely used in e-commerce recommender systems, which helps the online users to identify the right products to purchase. However, CF-based recommender systems suffer poor quality of recommendation due to the sparsity issue. To address this problem, in this paper we propose an adaptive recommendation method based on small-world implicit trust network. We first pres...

Trust has long been regarded as an important factor influencing users’ decision to buy a product in an online shop or to return to the shop for more product information. However, most notions of trust focus on the aspects of benevolence and integrity, and less on competence. Although benefits clearly exist for websites to employ competent recommender agents, the exact nature of these benefits t...

of the Thesis The Artificial Intelligence (AI) community has carried out a great deal of work on how AI can help people to find out what they want on the Internet. As a result, the idea of recommender systems has been widely accepted among users. The main task of a recommender system is to locate items, information sources and people related to the interest and preferences of a single person or...

Deciding whether to trust an information sources on the Web has been recognized as one of the main problems in today’s Information Society. In particular, assessing the credibility of news is a major research challenge. Typically, criteria such as freshness, relevance and viewer profile have been used by news services to rank news. However, these services do not deal with credibility from a qua...

With the advent of online social networks, recommender systems have became crucial for the success of many online applications/services due to their significance role in tailoring these applications to user-specific needs or preferences. Despite their increasing popularity, in general recommender systems suffer from the data sparsity and the cold-start problems. To alleviate these issues, in re...