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|Title:||Exploiting spatial, temporal, and semantic information for point-of-interest recommendation||Authors:||Yuan, Quan||Keywords:||DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications||Issue Date:||2014||Source:||Yuan, Q. (2014). Exploiting spatial, temporal, and semantic information for point-of-interest recommendation. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||With the prevalence of 3G & 4G services, people can easily share their opinions, moods, and activities with others via smartphones and tablets. As mobile devices are often GPS-enabled, a great quantity of user-generated content (UGC) with geographic locations has been accumulated, such as check-ins in location-based social networks (LBSNs), event-records in event-based social networks (EBSNs), and geo-annotated tweets on Twitter. Besides geographic location, UGC is often associated with timestamp and contains text content. The spatial, temporal and semantic information embedded in geo-annotated UGC can be exploited for a number of appealing applications and research problems. Point-of-interest (POI) recommendation is a representative one, which aims at recommending places that a target user has not visited before. Obviously, POI recommendation can help people explore new places and know their cities better. In addition, merchants can also benefit from it to deliver location-based advertisements and attract more customers. In recent years, a number of POI recommendation methods have been proposed, but most of them neglect contextual information, and make recommendations only based on user-POI check-in matrix. In real life, however, a user's preference to POIs is often influenced by her surroundings or context, such as time, companions, etc. For example, a user may prefer shopping malls to pubs in the afternoon, but may prefer pubs at night. Therefore, contextual information should be an important consideration for POI recommendation. In addition, a user may have specific requirement for recommendations sometimes, which directly reveals the user's preference. Thus, in this dissertation, we exploit the contextual information and requirements to recommend POIs for users. Specifically, we study three recommendation tasks that are relevant to the spatial, temporal, and semantic information of users. First, as human mobility is greatly influenced by time, we believe temporal influence is an important consideration for POI recommendation. We define a new problem, namely, time-aware POI recommendation, which aims to return a list of POIs for a user to visit at a specific time. In addition to temporal influence, human mobility is also influenced by geographic distance, e.g., people often visit their nearby places. To exploit both the temporal and spatial influences, we propose two algorithms, namely, User-based Collaborative Filtering with Temporal preference and smoothing Enhancement + Spatial influence with popularity Enhancement (UTE+SE) and Geographical-Temporal influences Aware Graph+Breadth-first Preference Propagation (GTAG-BPP), both of which are effective in making time-aware POI recommendations. We evaluate the performance of the proposed methods on two datasets, and the results show that the proposed methods outperform the state-of-the-art baselines significantly. Second, we observe that people often participate in activities and visit places together with others, e.g., watching movies with friends, and having dinner with colleagues. Thus, group POI recommendation is a realistic and important task, which aims at recommending POIs for a group of people. However, group recommendation is a challenging task, since group members may have different preferences, and how to balance their preferences is still an open problem. Furthermore, groups are often ad hoc, and the number of history records of a group may be very limited. The cold-start problem caused by ad hoc groups makes group recommendations even harder. To this end, we propose a Latent Dirichlet Allocation (LDA) based COnsensus Model (COM) to simulate the generative process of group activities and make POI recommendations for a group of users. Extensive experimental results on four real-world datasets validate that our model COM achieves superior recommendation accuracy comparing with five baselines. Third, when submitting recommendation requests, users may have clear requirements, e.g., dining or shopping, and the requirements can be formulated as short text. To make use of such information, we define a new task, namely, requirement-aware POI recommendation that generates a list of POIs for a target user based on her specific requirements. In addition, when target time is available, the recommendation results could be also time-aware. However, making time-aware and requirement-aware POI recommendations is non-trivial, as it calls for a model that can take into account the user, time, POI and words factors simultaneously. To solve this problem, we propose two frameworks, namely, a probabilistic Latent Semantic Analysis (pLSA) based model Who+Where+When+What (W4) and a Hierarchical Dirichlet Process (HDP) based model Enhanced W4 (EW^4), to model the complex interactions among the four factors, and make time-aware and requirement-aware POI recommendations. Empirical studies on two real-world datasets demonstrate our proposals outperform state-of-the-art approaches substantially. In summary, in this dissertation, we exploit spatial, temporal, and semantic information to recommend POIs to users, which is a natural but novel extension of exiting proposals on POI recommendation.||URI:||https://hdl.handle.net/10356/62666||DOI:||10.32657/10356/62666||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Theses|
Updated on May 15, 2021
Updated on May 15, 2021
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