A significance-based trust-aware recommendation approach

Trust-aware recommender systems have been widely used in recent years to improve the performance of traditional collaborative filtering systems. A common assumption of existing trust models is that all items have the same importance for all users. However, it is reasonable to expect that some items are more significant than others in making recommendations. Furthermore, the significance of an item is not the same for all users but varies depending on many factors such as the demographic characteristics of users. For example, an item that is important to women may not be important to men. Also, the significance of an item for an individual user is not static and can change throughout the life cycle (from childhood to old age). Thus, items that are currently important to a user may become less important in the future. In this paper, we propose a Significance-Based Trust-Aware Recommendation (SBTAR) approach, which uses a new trust measure based on the concept of item significance. The significance of an item for a user is measured with respect to the demographic context of the user. Thus, SBTAR can adapt to dynamic changes in user preferences. To model demographic context, SBTAR uses Shuffled Frog Leaping Algorithm (SFLA), which is a meta-heuristic optimization technique based on the social behavior of frogs. SFLA has the advantages of simplicity, fast convergence, strong global search ability and easy implementation. Experimental results show that the proposed approach is more effective and efficient than several state-of-the-art recommendation approaches. (C) 2019 Elsevier Ltd. All rights reserved.