Adaptive Successive POI Recommendation via Trajectory Sequences Processing and Long Short-Term Preference Learning

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 checkin 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 Fβmetric,respectively.

Enhanced Learning Resource Recommendation Based on Online Learning Style Model

Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences.This paper introduces a learning style model to represent features of online learners.It also presents an enhanced recommendation method named Adaptive Recommendation based on Online Learning Style(AROLS),which implements learning resource adaptation by mining learners’behavioral data.First,AROLS creates learner clusters according to their online learning styles.Second,it applies Collaborative Filtering(CF)and association rule mining to extract the preferences and behavioral patterns of each cluster.Finally,it generates a personalized recommendation set of variable size.A real-world dataset is employed for some experiments.Results show that our online learning style model is conducive to the learners’data mining,and AROLS evidently outperforms the traditional CF method.