关键词: 旅游路线推荐, 群体推荐, 签到数据, 基于位置的服务

Abstract:  With the wide use of mobile devices and location-based services, they have brought a mass of spatial-
temporal data, and the check-in data record the movement pattern of people in detail. Analyzing check-in data can improve the quality of location-based services, in which the trip recommendation is a significant research orientation. The existing studies of trip recommendation usually address the case of user’s traveling alone to meet the demand of individual user in the recommended route as much as possible. Travel together is a common phenomenon, and the research on group trip recommendation has the vital significance. For this requirement, this paper proposes a group trip recommendation with the goal of getting a trip route which can make high satisfaction for the overall group and satisfaction difference small between individuals in the group, and this route is optimal and is equitable for all the members. How to coordinate the demand of each user in the group is a vital issue in group trip recommendation. This paper proposes a dynamic aggregation preference (DAP) strategy for the dynamic feature when searching a route in view of the weakness in average strategy and no misery strategy which are usually employed in aggregation users’ preferences. According to the degree of satisfaction for current individual, DAP adjusts the group preference model dynamically that can guarantee the overall group high satisfaction and individuals satisfaction small difference. Based on DAP strategy, this paper constructs the route evaluation model, grades the satisfaction of route and returns the route with the highest score. Using check-in data sets from Gowalla and Foursquare social networking websites, this paper evaluates the efficiency of the proposed algorithms with extensive experiments under a wide range of parameter settings, verifies the effectiveness of the algorithms.

Key words: trip recommendation, group recommendation, check-in data, location-based service