Discovering Travel Community for POI Recommendation on Location‐Based Social Networks

Abstract

Point-of-interest (POI) recommendations are a popular form of personalized service in which users share their POI location and related content with their contacts in location-based social networks (LBSNs). The similarity and relatedness between users of the same POI type are frequently used for trajectory retrieval, but most of the existing works rely on the explicit characteristics from all users’ check-in records without considering individual activities. We propose a POI recommendation method that attempts to optimally recommend POI types to serve multiple users. The proposed method aims to predict destination POIs of a user and search for similar users of the same regions of interest, thus optimizing the user acceptance rate for each recommendation. The proposed method also employs the variable-order Markov model to determine the distribution of a user’s POIs based on his or her travel histories in LBSNs. To further enhance the user’s experience, we also apply linear discriminant analysis to cluster the topics related to “Travel” and connect to users with social links or similar interests. The probability of POIs based on users’ historical trip data and interests in the same topics can be calculated. The system then provides a list of the recommended destination POIs ranked by their probabilities. We demonstrate that our work outperforms collaborative-filtering-based and other methods using two real-world datasets from New York City. Experimental results show that the proposed method is better than other models in terms of both accuracy and recall. The proposed POI recommendation algorithms can be deployed in certain online transportation systems and can serve over 100,000 users.

A Hybrid Approach to Service Recommendation Based on Network Representation Learning

“…Recommendation system is an important information filtering method at present, among which collaborative filtering (CF) is widely used in large-scale recommendation engines due to its high efficiency, accuracy and scalability [6]. CF can be roughly divided into three categories: the model-based [7], the memory-based [8] and the context-based [16].…”

“…The results indicated that the method outperformed several conventional tag-based methods. To alleviate the sparsity for new and inactive users, Tang et al [16] proposed a POI recommendation method which attempts to recommend best POI types to multiple users. The purpose of this method is to predict the target POIs of users and search similar users with the same interest, so as to optimize the user acceptance rate of each recommendation.…”

Semantic‐Aware Top‐k Multirequest Optimal Route

“…POI recommendation is one of hot topics and it can provide better POIs for route planning. Lei Tang [22] et al proposed a personal POI recommendation method based on destination prediction. And Jianxin Li [23] proposed Personalized Influential Topic Search, or more succinctly PIT-Search.…”

“…After that, we add the keyword to the result set and assign the result set to pre set (lines [14][15][16][17]. After the initial step, the algorithm finds the service with lower priority through the last finished service and stores the service into the result set (lines [19][20][21][22][23][24][25][26][27][28]. Finally, the algorithm outputs the result set (line 29).…”

POI Recommendation Method Using Deep Learning in Location‐Based Social Networks

“…With the maturity of internet technology and the widespread application of global satellite positioning systems, locationbased social network (LBSN) has gradually received more and more attention and research. Recommending future potential points of interest (POI) to users by analyzing their existing historical check-in points has become a research hotspot in this field [1]. Specifically, personalized POI recommendation tasks can be divided into two types according to the recommendation basis.…”