Abstract
The interactions between group members often have a significant impact on the results of group recommendations. The traditional group recommendation algorithm does not consider the trust and social influence among users. It involves a low utilization rate of social relationship information, which leads to a low accuracy and satisfaction of group recommendations. Considering these issues, in this study, we propose a travel group recommendation model based on user trust and social influence. Based on the user trust relationship, this model defines the user direct and indirect trust and calculates the user global trust by combining the two trusts. Subsequently, the PageRank algorithm is used to calculate the social influence of users based on their interaction relationship history. Thereafter, a consensus model integrating the intra- and intergroup prediction scores is designed by integrating users’ global trust and social influence to realize group recommendations for tourist attractions. Comparison experiments with several well-known group recommendation models for datasets of different scenic spots in Beijing demonstrate that the proposed model provides a better recommendation performance.
Highlights
Online searches have become the main method for tourists to obtain information before traveling
This study proposes a tourist group recommendation model based on a social influence and user trust recommendation model, namely, TSTGR. is model considers the social influence of social networks and trust between the users in the group integration strategy, optimizes the differences within the group consensus, and realizes tourism destination recommendations
According to the social relationships of travel users, this study proposes a group recommendation framework that integrates user trust and social influence, as shown in Figure 1. is framework is mainly composed of three parts: a data acquisition module, preference modeling module, and group recommendation algorithm design module. e data collection module is mainly responsible for the collection and sorting of data for tourist attractions, as well as the collection and processing of social relationships between users. e preference modeling module mainly involves group discovery, trust modeling, and social influence modeling
Summary
Online searches have become the main method for tourists to obtain information before traveling. According to online evaluation information of tourists, the opinions and social relationships of users within a group are utilized to form a common decision-making mechanism and improve the accuracy and satisfaction of a tourism recommendation. Due to the complexity of attributes such as context, item, and user in travel recommendation, this paper employs users’ social influence to improve the weight of each user in the group user’s recommendation to improve the accuracy of recommendation. To overcome this issue, this study proposes a tourist group recommendation model based on a social influence and user trust recommendation model, namely, TSTGR. The validity of the proposed group recommendation algorithm is verified using a real dataset from Beijing
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