Context-Aware Recommendations for Point-of-Interests
AffiliationComputing and Information Systems
Document TypePhD thesis
Access StatusThis item is embargoed and will be available on 2022-06-24. This item is currently available to University of Melbourne staff and students only, login required.
© 2020 Jiayuan He
The rapidly growing location-based social networks allow web users to check-in at point- of-interests (POIs) and share their check-ins with the public. The large amount of web breadcrumbs left by users has enabled researchers to investigate human mobility patterns, which opens new research opportunities for incorporating better personalization into location-based services. In general, two types of recommendation tasks have been extensively investigated. The first type is POI recommendation which aims to provide a ranking list of POIs according to their attractiveness to a user. The second type is trip recommendation which aims to suggest an itinerary, i.e., an ordered sequence of POIs, for users. Developing recommendation models for POIs is challenging mainly due to three reasons. First, the observed POI visits of an individual user are limited in quantity. Second, users’ preferences over POIs are usually influenced by various contextual factors. Examples of these contextual factors include sequential contexts (i.e., the influence of a user’s recent POI visits on her next visit), temporal contexts (i.e., the influence of the visiting time on a user’s preference over POIs), and geographical contexts (i.e., the influence of a user’s geographical location on her preference over POIs). Finally, another reason that makes the development of POI recommendation models challenging is that the valuable information (e.g., the temporal contexts, the reviews left by users, and the geo-tagged photos post by users), which is useful to enhance the recommendation accuracy, has heterogeneous forms (e.g., the numerical, textual, and visual forms). In this thesis, we aim to address the following questions in the domain of POI recommendations: 1. How can we capture the complex interactions between users’ preferences and the contextual factors effectively and efficiently? 2. How can we model the temporal dynamics in users’ preferences over POIs given the limited observations of users’ historical POI visits? 3. How can we utilize the online reviews generated by users to improve the accuracy of POI recommendations? 4. How to generate personalized trip itineraries for users effectively and efficiently? To address the first research question, we propose a Gaussian process factorization model for POI recommendation. To further improve the scalability of the proposed model, we propose a query-aware Bayesian committee machine (QBCM) for scalable Gaussian progress regression. We show that the proposed QBCM model improves the prediction accuracy in Gaussian process regression by up to 23.3% comparing with state-of-the-art GP approximation models. To address the second research question, we propose time-modulated self-attentive network for time-aware next POI recommendation. The proposed model learns the relevance between users’ historical POI visits and their next POI visits via the self-attention mechanism, where the relevance is modulated by the impact of the temporal contexts. We show that the proposed model improves the recommendation accuracy by up to 17.1% while maintaining high training efficiency. To address the third research question, we propose a distillation framework for POI recommendation leveraging user reviews. The framework first uses a teacher model to extract the fine-grained sentiment orientations of the textual reviews left by users. Then the extracted information is fed into a light-weight student recommendation model via knowledge distillation. We show that the proposed model achieves competitive results compared with state-of-the-art review-based recommendation models. In particular, it can improve the accuracy in rating prediction by up to 10.1%. To address the final research question, we propose a unified trip recommendation framework which jointly considers the impact of three factors over the probability of a POI being visited in a trip, namely POI popularities, users’ personal preferences, and POI co-occurring probabilities. We show that the proposed trip recommendation framework consistently outperforms state-of-the-art algorithms, with an advantage of up to 43% in F1-score.
KeywordsRecommender systems; Location-Based social networks; Data mining; Trip planning; Spatial databases
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