Name
Abstract | ||
|---|---|---|
The increasing proliferation of location-based social networks brings about a huge volume of user check-in data, which facilitates the recommendation of points of interest (POIs). Time and location are the two most important contextual factors in the user’s decision-making for choosing a POI to visit. In this article, we focus on the spatiotemporal context-aware POI recommendation, which considers the joint effect of time and location for POI recommendation. Inspired by the recent advances in knowledge graph embedding, we propose a spatiotemporal context-aware and translation-based recommender framework (STA) to model the third-order relationship among users, POIs, and spatiotemporal contexts for large-scale POI recommendation. Specifically, we embed both users and POIs into a “transition space” where spatiotemporal contexts (i.e., a <time, location> pair) are modeled as translation vectors operating on users and POIs. We further develop a series of strategies to exploit various correlation information to address the data sparsity and cold-start issues for new spatiotemporal contexts, new users, and new POIs. We conduct extensive experiments on two real-world datasets. The experimental results demonstrate that our STA framework achieves the superior performance in terms of high recommendation accuracy, robustness to data sparsity, and effectiveness in handling the cold-start problem.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3295499 | ACM Trans. Inf. Syst. |
Keywords | Field | DocType |
POI recommendation, contextual modeling, location-based social networks, spatiotemporal aware | Information system,Knowledge graph,Embedding,Social network,Information retrieval,Computer science,Exploit,Robustness (computer science),Point of interest,Feature learning | Journal |
Volume | Issue | ISSN |
37 | 2 | 1046-8188 |
Citations | PageRank | References |
12 | 0.54 | 49 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tieyun Qian | 1 | 177 | 28.81 |
| Bei Liu | 2 | 26 | 12.94 |
| Nguyen Quoc Viet Hung | 3 | 515 | 43.34 |
| Hongzhi Yin | 4 | 1364 | 75.83 |