Abstract
Consider a group of users who would like to meet to a place in order to participate in an activity together (e.g., meet at a restaurant to dine). Such meeting point queries have been studied in the context of spatial databases, where typically the suggested points are the ones that minimize an aggregate traveling distance. Recently, meeting point queries have been enriched to take as input, besides the locations of users, also some preference criteria (e.g., expressed by some keywords). However, in many applications, a group of users may require a meeting point recommendation without explicitly specifying any preferences. Motivated by this, we study this scenario of group recommendation for such passive users. We use topic modeling to infer the preferences of the group on the different points of interest and combine these preferences with the aggregate spatial distance of the group members to the candidate points for recommendation in a unified search model. Then, we propose an extension of the R-tree index, called TAR-tree, that indexes the topic vectors of the places together with their spatial locations, in order to facilitate efficient group recommendation. We propose and compare three variants of the TAR-tree and a compression technique for the index, that improves its performance. The proposed techniques are evaluated on real data; the results demonstrate the efficiency and effectiveness of our methods.
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Acknowledgements
We thank the reviewers for their valuable comments. This work is partially supported by GRF Grants 17201414 and 17205015 from Hong Kong Research Grant Council. It has also received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 657347.
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Qian, Y., Lu, Z., Mamoulis, N., Cheung, D.W. (2017). P-LAG: Location-Aware Group Recommendation for Passive Users. In: Gertz, M., et al. Advances in Spatial and Temporal Databases. SSTD 2017. Lecture Notes in Computer Science(), vol 10411. Springer, Cham. https://doi.org/10.1007/978-3-319-64367-0_13
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DOI: https://doi.org/10.1007/978-3-319-64367-0_13
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