@Article{cmc.2024.055141,
AUTHOR = {Yali Si, Feng Li, Shan Zhong, Chenghang Huo, Jing Chen, Jinglian Liu},
TITLE = {Adaptive Successive POI Recommendation via Trajectory Sequences Processing and Long Short-Term Preference Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {81},
YEAR = {2024},
NUMBER = {1},
PAGES = {685--706},
URL = {http://www.techscience.com/cmc/v81n1/58331},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2024.055141}
}