An efficient ride-sharing recommendation for maximizing acceptance on geo-social data

• Published in: CCF transactions on pervasive computing and interaction (Online) Vol. 1; no. 4; pp. 240 - 249
• Main Authors: Tang, Lei, Han, Meng, Duan, Zongtao, Cai, Dandan
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.12.2019; Springer Nature B.V
• Subjects: Airports; Algorithms; Car pools; Computer Science; Data transmission; Maximization; Optimization; Psychological factors; Regular Paper; Semantics; Social networks; Travel; Travel time; User Interfaces and Human Computer Interaction; Social network; Acceptance; Destination prediction; Group recommendation; Ridesharing
• ISSN: 2524-521X; 2524-5228
• DOI: 10.1007/s42486-019-00015-0

Ride-sharing, which refers to assigning a set of riders for saving travel miles and alleviating traffic pressure, has drawn increasing attention. Existing works emphasize compatibility of potential riders on the basis of geographic proximity. They generally assume that no rejection would happen after the assignment is completed by the server. However, ignorance of psychological factors on ridesharing (e.g., trust on car mates) can lead to decrease rider acceptance. Thus, in this paper, we take the tendency of a rider to group with others into consideration and maximize riders’ acceptance when sharing a trip. Specifically, we formally define the problem of maximizing riders’ acceptance based on people ′ s interests, social links, and employ social networking to facilitate finding a ridesharing group for the rider with the largest acceptance. We propose a new ride-sharing mode to recommend groups that travel together from geo-social data streams. To optimize the recommendation, we develop a heterogenous travel network, based on a proposed destination-prediction algorithm, to mine the similar spatial movements among a set of riders. Then, we measure the willingness of riders for joining in a group using social context. Finally, we progressively select the riders with high acceptance to be in the top-k results. We present the results of applying framework on real world social media data from the Twitter. Computational results show our method is able to significantly reduce the travel time when ridesharing, while keeping a high level of acceptance on real-world datasets.