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
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To date, the majority of recommender systems (RSs) work on a single domain, such as exclusively for movies, books, etc. However, human preferences may span across multiple domains. Hence, consumption behaviors on related items from different domains can be useful to inform RS to make recommendations. This paper reports our efforts on uncovering the association between user preferences on related items across domains. In addition, we have also tested collaborative filtering technique on our cross-domain dataset for which results are reported here.
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- Adomavicius, G. and Tuzhilin, A., “Toward the Next Generation of Recommender Systems: a Survey of the State-of-the-art and Possible Extension,” IEEE Trans. Know. and Data Eng. 17, 6, pp. 734-749, 2005. CrossRef
- Balabanovic, M. and Shoham, Y., “Fab: Content-based Collaborative Recommendation,” CACM 40, 3, pp. 66-72, 1997.
- Beckett, A., Hewer, P. and Howcroft, B., “An Exposition of Consumer Behavior in the Financial Services Industry,” Int’l J. Bank Marketing 18, 1, pp. 15-26, 2000. CrossRef
- Berkovsky, S., Kuflik, T and Ricci, F., “Entertainment Personalization Mechanism through Cross-domain User Modeling,” in Proc. 1st Int’l Conf. Intelligent Technologies for Interactive Entertainment, LNAI 3814, pp. 215-219, 2005.
- Billsus, D. and Pazzani, M., “User Modeling for Adaptive News Access,” User Modeling and User-Adaptive Interaction 10, 2-3, pp. 147-180, 2000. CrossRef
- Breese, J.S., Heckerman, D. and Kadie, C., “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” in Proc. 14th Int’l Conf. Uncertainty in AI, pp. 43-52, 1998.
- Burke, R., “Hybrid Recommender Systems: Survey and Experiments,” User Modeling and User Adaptive Interaction 12, 4, pp. 331-370, 2002. CrossRef
- Cawley, G.C., and Talbot, N.L.C., “Efficient Leave-one-out Cross-validation of Kernel Fisher Discriminant Classifiers,” Pattern Recognition 36, 1, pp. 2585-2592, 2003. CrossRef
- Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D. and Sartin, M., “Combining Content-based and Collaborative Filters in an Online Newspaper,” in Workshop on Recommender system, in conjunction to SIGIR’99, 1999.
- Dietterich, T.G., “Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms,” Neural Computation 10, pp. 1895-1923, 1998. CrossRef
- Fox, S., Karnawat, K., Mydland, M., Dumais, S. and White, T., “Evaluating Implicit Measures to Improve Web Search,” ACM Trans. Inf. Sys. 23, pp. 147-168, 2005. CrossRef
- Herlocker, J., Konstan, J., Borchers, A. and Riedl, J., “An Algorithmic Framework for Performing Collaborative Filtering,” in Proc. 22nd Annual Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR’99), pp. 230-237, 1999.
- Herlocker, J., Konstan, J., Terveen, L. and Riedl, J., “Evaluating Collaborative Filtering Recommender Systems,” ACM Trans. Inf. Sys. 22, 1, pp. 5-53, 2004. CrossRef
- Joachims, T., Freitag, D. and Mitchell, T., “WebWatcher: A Tour Guide for the World Wide Web,” in Proc. 15th Int’l Joint Conf. on AI (IJCAI’97), pp. 770-775, 1997.
- Konstan, J.A.,Miller,B.N., Maltz, D.,Herlocker, J.L.,Gordon, L. R. and Riedl, J., “GroupLens: Applying Collaborative Filtering to Usenet News,” CACM 40, 3, pp. 77-87, 1997.
- Lariviere, B. and 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 Sys. with Applications 27, pp. 277-285, 2004. CrossRef
- Lekakos, G. and Giaglis, G., “Improving the Prediction Accuracy of Recommendation Algorithms: Approaches Anchored on Human Factors,” Interacting with Computers 18, 3, pp. 410-431, 2006. CrossRef
- Li, Y., Liu, L., and Li, X., “A Hybrid Collaborative Filtering Method for Multiple-interests and Multiple-content Recommendation in E-Commerce,” Expert Sys. with Applications 28, pp. 67-77, 2005. CrossRef
- Niedere, C., Stewart, A., Mehta, B. and Hemmje, M., “A Multi-dimensional, Unified User Model for Cross-system Personalization,” Workshop on environments for personalized information access, in conjunction to AVI’2004, 2004.
- Pazzani, P., “A Framework for Collaborative, Content-Based and Demographic Filtering,” AI Review 13, 5-6, pp. 393-408, 1999.
- Ratneshwar, S., Pechmann, C. and Shocker, A.D., “Goal-Derived Categories and the Antecedents of Across-Category Consideration,” J. Consumer Res. 23, 3, pp. 240-250, 1996. CrossRef
- Schein, A., Popescul, A., Ungar, L.H. and Pennock, D., “Methods and Metrics for Cold-start Recommendations,” in Proc. 25th annual Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR’02), 2002.
- Shardanand, U. and Maes, P., “Social Information Filtering: Algorithms for Automating ’Word of Mouth’,” in Proc. ACM SIGCHI Conf. Human Factors in Computing Sys., (ACM CHI’1995), pp. 210-217, 1995.
- Tang, T.Y., Winoto, P. and Chan, K.C.C., “Scaling Down Candidate Sets Based on the Temporal Feature of Items for Improved Hybrid Recommendations,” in Intelligent Techniques in Web Personalization (B. Mobasher and S. S. Anand Eds.), LNAI 3169, pp. 169-185, 2004.
- “Wiki on Cross-selling,” http://en.wikipedia.org/wiki/Cross-selling.
- Worthington, S. and Horne, S., “A New Relationship Marketing Model and Its Application in the Affinity Credit Card Market,” Int’l J. Bank Marketing 16, 1, pp. 39-44, 1998. CrossRef
- Wu, L., Liu, L., Li, J. and Li, Z., “Modeling User Multiple Interests by an Improved GCS Approach,” Expert Sys. with Applications 29, pp. 757-767, 2005. CrossRef
About this Article
- 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 Generation Computing
Volume 26, Issue 3 , pp 209-225
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- Verlag Omsha Tokio
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