Location Aware Keyword Query Suggestion Based on Document Proximity

IEEE Transactions on Knowledge and Data Engineering (TKDE)

Shuyao Qi¹    Dingming Wu²    Nikos Mamoulis¹

¹University of HongKong    ²Shenzhen University

Abstract

Keyword suggestion in web search helps users to access relevant information without having to know how to precisely express their queries. Existing keyword suggestion techniques do not consider the locations of the users and the query results; i.e., the spatial proximity of a user to the retrieved results is not taken as a factor in the recommendation. However, the relevance of search results in many applications (e.g., location-based services) is known to be correlated with their spatial proximity to the query issuer. In this paper, we design a location-aware keyword query suggestion framework. We propose a weighted keyword-document graph, which captures both the semantic relevance between keyword queries and the spatial distance between the resulting documents and the user location. The graph is browsed in a random-walk-with-restart fashion, to select the keyword queries with the highest scores as suggestions. To make our framework scalable, we propose a partition-based approach that outperforms the baseline algorithm by up to an order of magnitude. The appropriateness of our framework and the performance of the algorithms are evaluated using real data.

Fig. 1. LKS example.

Fig. 3. Illustration of Algorithm BA.

Fig. 6. Illustration of Algorithm PA.

Fig. 16. Varying partitioning methods and N.

Acknowledgements

This work was funded by EC grant 657347/H2020-MSCA-IF-2014 and by GRF grant 17205015 from Hong Kong RGC. Dingming Wu is the corresponding author.

Bibtex

@INPROCEEDINGS{7498428,

author={Qi, Shuyao and Wu, Dingming and Mamoulis, Nikos},

booktitle={2016 IEEE 32nd International Conference on Data Engineering (ICDE)},

title={Location aware keyword query suggestion based on document proximity},

year={2016},

pages={1566-1567},

doi={10.1109/ICDE.2016.7498428},

}

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