Trust-Based Recommendation for Shared Mobility Systems Based on a Discrete Self-Adaptive Neighborhood Search Differential Evolution Algorithm

Abstract

Safety is one concern that hinders the acceptance of ridesharing in the general public. Several studies have been conducted on the trust issue in recent years to relieve this concern. The introduction of trust in ridesharing systems provides a pragmatic approach to solving this problem. In this study, we will develop a trust-aware ridesharing recommender system decision model to generate recommendations for drivers and passengers. The requirements of trust for both sides, drivers and passengers, are taken into consideration in the decision model proposed in this paper. The decision model considers the factors in typical ridesharing systems, including vehicle capacities, timing, location and trust requirements, etc. The decision model aims to determine the shared rides that minimize cost while respecting the trust and relevant constraints. As the decision problem is a nonlinear integer programming problem, we combine a self-adaptive neighborhood search with Differential Evolution to develop an algorithm to solve it. To assess the effectiveness of the proposed algorithm, several other evolutionary computation approaches are also applied to solve the same problem. The effectiveness assessment is done based on the performance of applying different algorithms to find solutions for test cases, to provide a guideline for selecting a proper solution approach.

Recommendation System based on User Trust and Ratings

“…The merge of model-based collaborative filtering methods with trust relationships to improve the accuracy of recommendations has recently become a very popular research topic, especially using the matrix factorization technique due to its high precision and ease of use contribution to alleviating the problem of sparsity data better than other techniques [3,8]. Many researchers have exploited this technique to learn about latent features of users and well-known ratings items, and to merge social relationships between users with rating data using different techniques.…”

“…Many researchers have exploited this technique to learn about latent features of users and well-known ratings items, and to merge social relationships between users with rating data using different techniques. The researchers proposed in [8] a "SoRec" model which integrates a social network database into a probability matrix factorization model by simultaneously analyzing the rating matrix and the social trust matrix by sharing the matrix of features latent to a user [10]. Their empirical analysis shows that their method is superior to the basic matrix factorization model and other memory-based methods that take advantage of trust relationships, but that true recommendation processes are not reflected in this model.…”

“…SoRec [8] The method analyses the rating matrix and the users' social trust matrix by sharing the same space of latent features.…”

“…Therefore, when predicting a user's preferences for an item, it does make more sense to consider both the trustor's preferences and the trustee's performances at the same time. However, in the previous studies, the methods modelled the users using a single case [8], or by separately considering the two user cases [7]. In other words, no distinction is made between different cases of the user as trustee or trustor in the ratings generation process.…”

Improving Acceptability of Cost Savings Allocation in Ridesharing Systems Based on Analysis of Proportional Methods

“…[24]. The incorporation of social preferences into ridesharing decisions was studied by Saisubramanian et al in [25] and the consideration of trust in the ridesharing system was studied by Hsieh in [26]. A study on sharing rides with friends was available in the work [27] by Bistaffa et al A review of user behaviors/characteristics as well as social-economic impacts of the shared transport mode was available in the paper [28] by Sun et al Recent developments in studies on ridesharing can be found in the paper [29] by Hyland and Mahmassani. Although cities may benefit from ridesharing, stakeholders such as drivers and riders may adopt the ridesharing transport mode for a variety of reasons.…”