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A recommendation engine for travel products based on topic sequential patterns

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Abstract

Travel products recommendation has become one of emerging issues in the realm of recommendation systems. The widely-used collaborative filtering algorithms are usually difficult to be used for recommending travel products due to a number of reasons, including (1) the content of travel products is very complex, (2) the user-item matrix is extremely sparse, and (3) the cold-start users are widely existing. To tackle these issues, we try to exploit Web server logs for generating recommendation, and present a novel recommendation engine (SECT for short) for travel products based on topic sequential patterns. In detail, we first extract topics from semantic description of every Web page. Then, we mine topic frequent sequential patterns and their target products to form click patterns library. At last, we propose a Markov n-gram model for matching the real-time click-stream of users with the click patterns library and thus computing recommendation scores. Experimental results on a real-world travel dataset demonstrate that the SECT prevails over the state-of-art baseline algorithms. In particular, SECT shows merits in improving the both coverage and accuracy for recommending products to cold-start users. Also, SECT is effective to recommend long tail items and outperform baseline algorithms.

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Notes

  1. http://www.wttc.org.

  2. http://www.tuniu.com.

  3. http://grouplens.org/datasets/movielens/100k/.

  4. http://mahout.apache.org/.

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Acknowledgments

This work was partially supported by the National Key Research and Development Program of China (2016YFB1000901), National Natural Science Foundation of China (91646204, 71571093, 71372188), National Center for International Joint Research on E-Business Information Processing (2013B01035), and Industry Projects in Jiangsu S&T Pillar Program (BE2014141).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Guixiang Zhu

  2. Jiangsu Provincial Key Laboratory of E-Business, Nanjing University of Finance and Economics, Nanjing, China

    Jie Cao, Changsheng Li & Zhiang Wu

Authors
  1. Guixiang Zhu
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  2. Jie Cao
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  3. Changsheng Li
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  4. Zhiang Wu
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Correspondence to Jie Cao.

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Zhu, G., Cao, J., Li, C. et al. A recommendation engine for travel products based on topic sequential patterns. Multimed Tools Appl 76, 17595–17612 (2017). https://doi.org/10.1007/s11042-017-4406-6

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  • DOI: https://doi.org/10.1007/s11042-017-4406-6

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