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International Conference on Database Systems for Advanced Applications

DASFAA 2017: Database Systems for Advanced Applications pp 507–524Cite as

Location-Aware News Recommendation Using Deep Localized Semantic Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10177))

Abstract

With the popularity of mobile devices and the quick growth of the mobile Web, users can now browse news wherever they want, so their news preferences are usually strongly correlated with their geographical contexts. Consequently, many research efforts have been put on location-aware news recommendation; the explored approaches can mainly be divided into physical distance-based and geographical topic-based ones. As for geographical topic-based location-aware news recommendation, ELSA is the state-of-the-art geographical topic model: it has been reported to outperform many other topic models, e.g., BOW, LDA, and ESA. However, the Wikipedia-based topic space in ELSA suffers from the problems of high dimensionality, sparsity, and redundancy, which greatly degrade the recommendation performance of ELSA. Therefore, to overcome these problems, in this work, we propose three novel geographical topic feature models, CLSA, ALSA, and DLSA, which integrate clustering, autoencoders, and recommendation-oriented deep neural networks, respectively, with ELSA to extract dense, abstract, low dimensional, and effective topic features from the Wikipedia-based topic space for the representation of news and locations. Experimental results show that (i) CLSA, ALSA, and DLSA all greatly outperform the state-of-the-art geographical topic model, ELSA, in location-aware news recommendation in terms of both the recommendation effectiveness and efficiency; (ii) Deep Localized Semantic Analysis (DLSA) achieves the most significant improvements: its precision, recall, MRR, and MAP are all about 3 times better than those of ELSA; while its recommendation time-cost is only about 1/29 of that of ELSA; and (iii) DLSA, ALSA, and CLSA can also remedy the “cold-start” problem by uncovering users’ latent news preferences at new locations.

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Notes

  1. 1.

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Acknowledgments

This work is supported by the Mutual Project of Beijing Municipal Education Commission. Thomas Lukasiewicz and Zhenghua Xu are supported by the UK EPSRC Grants EP/J008346/1, EP/L012138/1, and EP/M025268/1, and by The Alan Turing Institute under the EPSRC Grant EP/N510129/1.

Author information

Authors and Affiliations

  1. Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China

    Cheng Chen & Xiangwu Meng

  2. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China

    Cheng Chen & Xiangwu Meng

  3. Department of Computer Science, University of Oxford, Oxford, UK

    Thomas Lukasiewicz & Zhenghua Xu

Authors
  1. Cheng Chen
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  2. Thomas Lukasiewicz
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  3. Xiangwu Meng
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  4. Zhenghua Xu
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Corresponding author

Correspondence to Xiangwu Meng .

Editor information

Editors and Affiliations

  1. Arizona State University , Tempe - Phoenix, Arizona, USA

    Selçuk Candan

  2. Hong Kong University of Science and Tech , Hong Kong, China

    Lei Chen

  3. Aalborg University , Aalborg, Denmark

    Torben Bach Pedersen

  4. University of New South Wales , Sydney, New South Wales, Australia

    Lijun Chang

  5. The University of Queensland , Brisbane, Queensland, Australia

    Wen Hua

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Chen, C., Lukasiewicz, T., Meng, X., Xu, Z. (2017). Location-Aware News Recommendation Using Deep Localized Semantic Analysis. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10177. Springer, Cham. https://doi.org/10.1007/978-3-319-55753-3_32

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  • DOI: https://doi.org/10.1007/978-3-319-55753-3_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55752-6

  • Online ISBN: 978-3-319-55753-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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