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Geographical Information in a Multi-domain Recommender System

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Web-Age Information Management (WAIM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8597))

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Abstract

Multi-domain recommendation is more challenging than that in the traditional single-domain one. In our previous study on the cross-domain recommendation, we have uncovered the tradeoff between the accuracy and the coverage of the recommendation. Later, we have also reported our findings on uncovering the association between user’s interests of items across domains that are related to each other to a certain degree using another dataset collected from users with different demographic information. In this paper, we further discuss the comparison between our previous two experimental results and some practical implications in the design of recommendation systems.

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References

  1. Chang, L.C.: Subcultural influence on Chinese negotiation styles. J. Glob. Bus. Manage. 2(3), 189–195 (2006)

    Google Scholar 

  2. Chang, L.C.: Sub-cultural business negotiation: a Taiwanese and Japanese-Chinese case study. Afr. J. Bus. Manage. 5(2), 389–393 (2011)

    Google Scholar 

  3. Herlocker, J., Konstan, J., Riedl, J.: Explaining collaborative filtering recommendations. In: Proceedings of the 2000 Conference on Computer Supported Cooperative Work (CSCW’2000), Philadelphia, PA, USA, pp. 241–250 (2000)

    Google Scholar 

  4. Herlocker, J., Konstan, J., Terveen, L., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)

    Article  Google Scholar 

  5. Jozsa, L., Insch, A., Krisjanous, J., Fam, K.: Beliefs about advertising in China: empirical evidence from Hong Kong and Shanghai consumers. J. Consum. Mark. 27(7), 594–603 (2010)

    Article  Google Scholar 

  6. Lariviere, B., Van den Poel, D.: Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: the case of financial services. Expert Syst. Appl. 27, 277–285 (2004)

    Article  Google Scholar 

  7. Li, Y., Liu, L., Li, X.: A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce. Expert Syst. Appl. 28, 67–77 (2005)

    Article  Google Scholar 

  8. McNee, S., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: The Extended Abstracts of the 2006 ACM Conference on Human Factors in Computing Systems (CHI 2006), Montreal, Canada, pp. 1097–1101 (2006a)

    Google Scholar 

  9. McNee, S., Riedl, J., Konstan, J.A.: Making recommendations better: an analytic model for human-recommender interaction. In: The Extended Abstracts of the 2006 ACM Conference on Human Factors in Computing Systems (CHI 2006), Montreal, Canada, pp. 1103–1108 (2006b)

    Google Scholar 

  10. Ramasamy, B., Au, A., Yeung, M.: Managing Chinese consumers’ value profiles: a comparison between Shanghai and Hong Kong. Cross Cult. Manage. Int. J. 17(3), 257–267 (2010)

    Article  Google Scholar 

  11. Ratneshwar, S., Pechmann, C., Shocker, A.D.: Goal-derived categories and the antecedents of across-category consideration. J. Consum. Res. 23(3), 240–250 (1996)

    Article  Google Scholar 

  12. Schein, A., Popescul, A., Ungar, L.H., Pennock, D.: Methods and metrics for cold-start recommendations. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’02) (2002)

    Google Scholar 

  13. Tang, T.Y., Winoto, P., Ye, R.Z.: Analysis of a multi-domain recommender system. In: Proceedings of the 3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMIA2011), Macao, pp. 471–476 (2011)

    Google Scholar 

  14. Tsang, A.S.L., Zhuang, G., Li, F., Zhou, N.: A comparison of shopping behavior in Xi’an and Hong Kong Malls: Utilitarian versus Non-Utilitarian Shoppers. J. Int. Consum. Mark. 16(1), 29–46 (2003)

    Article  Google Scholar 

  15. Winoto, P., Tang, T.: If you like the Devil wears Prada the book, will you also enjoy the Devil wears Prada the movie? a study of cross-domain recommendations. New Gener. Comput. 26(3), 209–225 (2008)

    Article  Google Scholar 

  16. Ziegler, C.-N., McNee, S., Konstan, J., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International World Wide Web Conference (WWW ‘05), pp. 22–32 (2005)

    Google Scholar 

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Correspondence to Tiffany Y. Tang .

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Tang, T.Y., Winoto, P., Ye, R.Z. (2014). Geographical Information in a Multi-domain Recommender System. In: Chen, Y., et al. Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science(), vol 8597. Springer, Cham. https://doi.org/10.1007/978-3-319-11538-2_29

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  • DOI: https://doi.org/10.1007/978-3-319-11538-2_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11537-5

  • Online ISBN: 978-3-319-11538-2

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