A Novel Travel Group Recommendation Model Based on User Trust and Social Influence

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.

Sequential group recommendations based on satisfaction and disagreement scores

Recently, group recommendations have gained much attention. Nevertheless, most approaches consider only one round of recommendations. However, in a real-life scenario, it is expected that the history of previous recommendations is exploited to tailor the recommendations towards meeting the needs of the group members. Such history should include not only which items the system suggested, but also the reaction of the members to these items. This work introduces the problem of sequential group recommendations, by exploiting the concept of satisfaction and disagreement. Satisfaction describes how well the group received the suggested items. Disagreement describes the satisfaction bias among the group members. We utilize these concepts in three new aggregation methods, SDAA, SIAA and Average+, designed to address the specific challenges introduced by sequential group recommendations. We experimentally show the effectiveness of our methods using big real datasets for both stable and ephemeral groups.