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A two-stage multiple-factor aware method for travel product recommendation

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Abstract

The great quantity of travel products available online has increased demand for travel product recommendation system. Due to the relatively high value and time cost of travel products, users consider more factors (personal preference, social preference and seasonality factor etc.) in making this type of low-frequent purchase decisions, compared to other products (e.g. music, movies or news). Thus, recommending travel products generally faces sparsity and complexity problems. In this study, we propose a two-stage multiple-factor aware method named TSMFA. In the topic stage, a user-topic matrix is constructed using travel products’ topic attributions to alleviate sparsity problem, while a preference-aware topic selection is introduced to consider both social and personal preference in recommendation. In the product stage, seasonal prevalence is employed to adjust the recommended product order to incorporate seasonality factor. The proposed method is validated with real transaction dataset from a leading OTA (Online Travel Agent) website in western China. The experimental results demonstrate that it outperforms the state-of-the-art recommendation methods in terms of effectiveness and usefulness.

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References

  1. Agrawal S, Bamnote G, Satarkar S (2016) A hybrid clustering based collaborative filtering (cf) approach. In: Proceedings of the 2nd international conference on information and communication technology for competitive strategies, p 21. ACM

  2. Barragáns-Martínez AB, Costa-Montenegro E, Burguillo JC, Rey-López M, Mikic-Fonte FA, Peleteiro A (2010) A hybrid content-based and item-based collaborative filtering approach to recommend tv programs enhanced with singular value decomposition. Inf Sci 180(22):4290–4311

    Article  Google Scholar 

  3. Bokde D, Girase S, Mukhopadhyay D (2015) Matrix factorization model in collaborative filtering algorithms: a survey. Procedia Computer Science 49:136–146

    Article  Google Scholar 

  4. Borràs J, Moreno A, Valls A (2014) Intelligent tourism recommender systems: A survey. Expert Syst Appl 41(16):7370–7389

    Article  Google Scholar 

  5. Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pp 43–52. Morgan Kaufmann Publishers Inc

  6. Burke R (2002) Hybrid recommender systems: Survey and experiments. User Model User-Adap Inter 12(4):331–370

    Article  Google Scholar 

  7. Cui L, Huang W, Yan Q, Yu FR, Wen Z, Lu N (2017) A novel context-aware recommendation algorithm with two-level svd in social networks. Future Generation Computer Systems

  8. De Pessemier T, Dhondt J, Martens L (2017) Hybrid group recommendations for a travel service. Multimedia Tools and Applications 76(2):2787–2811

    Article  Google Scholar 

  9. Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans Inf Syst (TOIS) 22(1):143–177

    Article  Google Scholar 

  10. Ge Y, Liu Q, Xiong H, Tuzhilin A, Chen J (2011) Cost-aware travel tour recommendation. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, pp 983–991. ACM

  11. Ge Y, Xiong H, Tuzhilin A, Liu Q (2014) Cost-aware collaborative filtering for travel tour recommendations. ACM Trans Inf Syst (TOIS) 32(1):4

    Article  Google Scholar 

  12. Herlocker JL, Konstan JA, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval, pp 230–237. ACM

  13. Jin R, Chai JY, Si L (2004) An automatic weighting scheme for collaborative filtering. In: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pp 337–344. ACM

  14. Li H, Hong R, Wu Z, Ge Y (2016) A spatial-temporal probabilistic matrix factorization model for point-of-interest recommendation. In: Proceedings of the 2016 SIAM international conference on data mining, pp 117–125. SIAM

  15. Li YM, Lin LF, Ho CC (2017) A social route recommender mechanism for store shopping support. Decis Support Syst 94:97–108

    Article  Google Scholar 

  16. Liu Q, Ge Y, Li Z, Chen E, Xiong H (2011) Personalized travel package recommendation. In: 2011 IEEE 11th international conference on data mining (ICDM), pp 407–416. IEEE

  17. Liu Q, Chen E, Xiong H, Ge Y, Li Z, Wu X (2014) A cocktail approach for travel package recommendation. IEEE Trans Knowl Data Eng 26(2):278–293

    Article  Google Scholar 

  18. Lucas JP, Luz N, Moreno MN, Anacleto R, Figueiredo AA, Martins C (2013) A hybrid recommendation approach for a tourism system. Expert Syst Appl 40(9):3532–3550

    Article  Google Scholar 

  19. Lundtorp S (2001) Measuring tourism seasonality. Seasonality in tourism 3 (3):23–50

    Article  Google Scholar 

  20. Moutinho L (1987) Consumer behaviour in tourism. Eur J Mark 21(10):5–44

    Article  Google Scholar 

  21. Paunovic I, Radojevic M (2014) Towards green economy: balancing market and seasonality of demand indicators in serbian mountain tourism product development. In: Faculty of tourism and hospitality management in opatija. Biennial international congress. Tourism & hospitality industry, p 601. University of Rijeka, Faculty of Tourism & Hospitality Management

  22. Ren X, Song M, Haihong E, Song J (2017) Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation. Neurocomputing 241:38–55

    Article  Google Scholar 

  23. Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp 175–186. ACM

  24. Ricci F (2002) Travel recommender systems. IEEE Intell Syst 17(6):55–57

    Google Scholar 

  25. Sarwar B, Karypis G, Konstan J, Riedl J (2000) Application of dimensionality reduction in recommender system-a case study. Tech. rep. Minnesota Univ Minneapolis Dept of Computer Science

  26. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp 285–295. ACM

  27. Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Advances in Artificial Intelligence 2009:4

    Article  Google Scholar 

  28. Tan F, Li L, Zhang Z, Guo Y (2016) A multi-attribute probabilistic matrix factorization model for personalized recommendation. Pattern Anal Applic 19 (3):857–866

    Article  MathSciNet  Google Scholar 

  29. Ungar LH, Foster DP (1998) Clustering methods for collaborative filtering. In: AAAI workshop on recommendation systems, vol 1, pp 114–129

  30. Vargas S, Castells P (2011) Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the fifth ACM conference on Recommender systems, pp 109–116. ACM

  31. Verma SK, Mittal N, Agarwal B (2013) Hybrid recommender system based on fuzzy clustering and collaborative filtering. In: 2013 4th international conference on computer and communication technology (ICCCT), pp 116–120. IEEE

  32. Wang J, De Vries AP, Reinders MJ (2006) Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pp 501–508. ACM

  33. Xue GR, Lin C, Yang Q, Xi W, Zeng HJ, Yu Y, Chen Z (2005) Scalable collaborative filtering using cluster-based smoothing. In: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pp 114–121. ACM

  34. Yun Y, Hooshyar D, Jo J, Lim H (2017) Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review. Journal of Information Science p 0165551517692955. https://doi.org/10.1177/0165551517692955

    Article  Google Scholar 

  35. Zhang Y, Chen W, Yin Z (2013) Collaborative filtering with social regularization for tv program recommendation. Knowl-Based Syst 54:310–317. https://doi.org/10.1177/0165551517692955

    Article  Google Scholar 

  36. Zhang H, Ge D, Zhang S (2018) Hybrid recommendation system based on semantic interest community and trusted neighbors. Multimedia Tools and Applications 77(4):4187–4202

    Article  Google Scholar 

  37. Zhu G, Cao J, Li C, Wu Z (2017) A recommendation engine for travel products based on topic sequential patterns. Multimedia Tools and Applications 76 (16):17595–17612. https://doi.org/10.1177/0165551517692955 https://doi.org/10.1177/0165551517692955

    Article  Google Scholar 

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Acknowledgements

This work is supported by the National Nature Science Foundation of China (Grant No. 91746111, Grant No.71702143), Ministry of Education & China Mobile Joint Research Fund Program (No. MCM20160302), Shaanxi provincial development and Reform Commission (No. SFG2016789), Xi’an Municipal Science & Technology Commission (No. 2017111SF/RK005-(7)), Natural Science Foundation of Shaanxi (NO. 2017JQ7004), China Postdoctoral Science Fund (No. 2016M602840).

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Authors and Affiliations

  1. School of Management, Northwestern Polytechnical University, Xi’an, China

    Jun An & Songzheng Zhao

  2. School of Management, Xi’an Jiaotong University, Xi’an, China

    Xiaoni Lu

  3. School of Information Technology and Management, University of International Business and Economics, Beijing, China

    Ningning Liu

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  1. Jun An
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  2. Songzheng Zhao
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Correspondence to Xiaoni Lu.

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An, J., Zhao, S., Lu, X. et al. A two-stage multiple-factor aware method for travel product recommendation. Multimed Tools Appl 77, 28991–29012 (2018). https://doi.org/10.1007/s11042-018-5992-7

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  • DOI: https://doi.org/10.1007/s11042-018-5992-7

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