With the rapid development and extensive application of Web services, various approaches for Web service recommendation have been proposed in the past. However, the traditional methods only utilize the information of the user-service rating matrix but ignore the trust relations between users, so their recommendation precision is often unsatisfactory, and, furthermore, most of these methods lack the ability to distinguish the credibility of recommendation. To address the problems, we proposed a personalized service recommendation based on trust relationship. In particular, our approach takes into account user experience, interest background, recommendation effect, and evaluation tendency in the formalization of trust relationship, and moreover it can filter out useless or suspected services by exploiting trust relationships between users. To verify the proposed approach, we conducted experiments by using a real-world Web services set. The experimental results show that our proposed approach leads to a substantial increase in the precision and the credibility of service recommendations.

Abstract:In order to alleviate the pressure of information overload and enhance consumer satisfaction, personalization recommendation has become increasingly popular in recent years. As a result, various approaches for recommendation have been proposed in the past few years. However, traditional recommendation methods are still troubled with typical issues such as cold start, sparsity, and low accuracy. To address these problems, this paper proposed an improved recommendation method based on trust relationships in social networks to improve the performance of recommendations. In particular, we define trust relationship afresh and consider several representative factors in the formalization of trust relationships. To verify the proposed approach comprehensively, this paper conducted experiments in three ways. The experimental results show that our proposed approach leads to a substantial increase in prediction accuracy and is very helpful in dealing with cold start and sparsity.

An electronic transition-based bare bones particle swarm optimization (ETBBPSO) algorithm is proposed in this paper. The ETBBPSO is designed to present high precision results for high dimensional single-objective optimization problems. Particles in the ETBBPSO are divided into different orbits. A transition operator is proposed to enhance the global search ability of ETBBPSO. The transition behavior of particles gives the swarm more chance to escape from local minimums. In addition, an orbit merge operator is proposed in this paper. An orbit with low search ability will be merged by an orbit with high search ability. Extensive experiments with CEC2014 and CEC2020 are evaluated with ETBBPSO. Four famous population-based algorithms are also selected in the control group. Experimental results prove that ETBBPSO can present high precision results for high dimensional single-objective optimization problems.

No abstract