A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations

Abstract

Location-based Social Networks (LBSNs) enable users to socialize with friends and acquaintances by sharing their check-ins, opinions, photos, and reviews. Huge volume of data generated from LBSNs opens up a new avenue of research that gives birth to a new sub-field of recommendation systems, known as Point-of-Interest (POI) recommendation. A POI recommendation technique essentially exploits users' historical check-ins and other multi-modal information such as POI attributes and friendship network, to recommend the next set of POIs suitable for a user. A plethora of earlier works focused on traditional machine learning techniques by using hand-crafted features from the dataset. With the recent surge of deep learning research, we have witnessed a large variety of POI recommendation works utilizing different deep learning paradigms. These techniques largely vary in problem formulations, proposed techniques, used datasets and features, etc. To the best of our knowledge, this work is the first comprehensive survey of all major deep learning-based POI recommendation works. Our work categorizes and critically analyzes the recent POI recommendation works based on different deep learning paradigms and other relevant features. This review can be considered a cookbook for researchers or practitioners working in the area of POI recommendation.

Developing A Contextual Combinational Approach for Predictive Analysis of Users Mobile Phone Trajectory Data in LBSNs

“…The problem that the RNN faces is the exploding and vanishing gradients; therefore, it cannot capture long-term preferences [2,11]. The problems can be solved by longshort term memory (LSTM), which employs a gate mechanism and can capture long-term preferences [1,23].…”

“…LSTM resolves the problems of the RNN, but it has three gates thus the training of an LSTM-based model is slower and requires a large amount of training data. GRU[4,6,23,40] has updated and reset gates in the network, dealing with the update degree of each hidden state. In fact, it determines which information should pass to the next state[2,3].…”

POIs Category Recommendation for Cultural Country Travel Enterprises Based on Check-In Information

“…In (Islam, M., et al, 2020), Location-Based Social Networks (LBSNs) allow users to socialize by sharing historical information such as their clicks and comments with friends. The massive data generated by LNSNs has brought new development space for recommendation system, and derived a new field-POI recommendation.…”