TY  - EJOU
AU  - Si, Yali 
AU  - Li, Feng 
AU  - Zhong, Shan 
AU  - Huo, Chenghang 
AU  - Chen, Jing 
AU  - Liu, Jinglian 

TI  - Adaptive Successive POI Recommendation via Trajectory Sequences Processing and Long Short-Term Preference Learning
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 81
IS  - 1
SN  - 1546-2226

AB  - Point-of-interest (POI) recommendations in location-based social networks (LBSNs) have developed rapidly by incorporating feature information and deep learning methods. However, most studies have failed to accurately reflect different users’ preferences, in particular, the short-term preferences of inactive users. To better learn user preferences, in this study, we propose a long-short-term-preference-based adaptive successive POI recommendation (LSTP-ASR) method by combining trajectory sequence processing, long short-term preference learning, and spatiotemporal context. First, the check-in trajectory sequences are adaptively divided into recent and historical sequences according to a dynamic time window. Subsequently, an adaptive filling strategy is used to expand the recent check-in sequences of users with inactive check-in behavior using those of similar active users. We further propose an adaptive learning model to accurately extract long short-term preferences of users to establish an efficient successive POI recommendation system. A spatiotemporal-context-based recurrent neural network and temporal-context-based long short-term memory network are used to model the users’ recent and historical check-in trajectory sequences, respectively. Extensive experiments on the Foursquare and Gowalla datasets reveal that the proposed method outperforms several other baseline methods in terms of three evaluation metrics. More specifically, LSTP-ASR outperforms the previously best baseline method (RTPM) with a 17.15% and 20.62% average improvement on the Foursquare and Gowalla datasets in terms of the <i>F</i><sub><i>β</i></sub> metric, respectively.
KW  - Location-based social networks; adaptive successive point-of-interest recommendation; long short-term preference; trajectory sequences

DO  - 10.32604/cmc.2024.055141