在推薦系統中協同過濾 (Collaborative filtering) 是目前最廣泛使用的方法之一，而此方法最重要的組成部分便是透過使用者項目矩陣 (User-item matrix) 找到相似的使用者或是項目，並以此來進行產品的推薦。然而傳統協同過濾的方法在計算目標使用者與其他使用者的相似度時，並沒有考慮到目標項目與其他共同評分項目 (Co-rated items) 之間的關係，更精確地說它們給予共同評分過的項目相同的權重。但是我們認為在計算使用者之間的相似度時，目標項目與其他共同評分項目之間的關係，是一個非常重要的因素。鑑此，我們提出一個新的相似度計算方式，此方法不僅考慮了目標項目與其他共同評分項目之間的關聯性，以及共同評分的比例並且針對冷開機問題 (Cold-start problem) 結合了人工生成資訊 (Profile expansion)的方法。而從實驗的結果也顯示出我們所提出的方法不只對於一般情況下有很好的推薦效果，對於冷開機狀況下仍然有不錯的推薦準確度。

Collaborative filtering is one of the most widely used methods to provide product recommendation in online stores. The key component of the method is to find similar users or items by using user-item matrix so that products can be recommended based on the similarities. However, traditional collaborative filtering approaches compute the similarity between a target user and the other user without considering a target item. More specifically, they give an equal weight to each of the items which are rated by both users. However, we think that the similarity between the target item and each of the co-rated items is a very important factor when we calculate the similarity between two users. Therefore, in this paper we propose a new similarity function that takes similarities between a target item and each of the co-rated items and the proportion of common ratings into account. In addition, we also combine the item genre to the profile expansion to reinforce our model in order to deal with the cold-start problem. Experimental results from MovieLens dataset show that the method improves accuracy of recommender system significantly.

1. Keunho, C., and Suh, Y.: A new similarity function for selecting neighbors for each target item in collaborative filtering. Knowledge-Based Systems, pp. 146-153 (2013)
2. Balabanovi´c, M. and Shoham, Y.: Content-based, collaborative recommendation. Communications of the ACM, vol. 40, no. 3, pp. 66–72 (1997)
3. Hill, W., Stead, L., Rosenstein, M., and Furnas, G.: Recommending and evaluating choices in a virtual community of use. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, vol. 1, pp. 194-201 (1995)
4. Zhao, X. W., Guo, Y., He, Y., Jiang, H., Wu, Y., & Li, X.: We know what you want to buy: a demographic-based system for product recommendation on microblogs. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1935-1944 (2014)
5. De Campos, L. M., Fernández-Luna, J. M., Huete, J. F., & Rueda-Morales, M. A.: Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks. International Journal of Approximate Reasoning, vol. 51, no.7, pp. 785-799 (2010)
6. Liu, H., Hu, Z., Mian, A., Tian, H., & Zhu, X.: A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based Systems, pp. 156-166 (2014)
7. Adomavicius, E., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749 (2005)
8. Ryan, P.B., Bridge, D.: Collaborative recommending using formal concept analysis. Knowledge Based Systems, pp. 309–315 (2006)
9. Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. SIGIR, pp. 253–260 (2002)
10. Park, S.T., Chu, W.: Pairwise preference regression for cold-start recommendation. Proceedings of the 2009 ACM Conference on Recommender Systems, pp. 21–28 (2009)
11. Hyung, J.A.: A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences, pp. 17837–17851 (2008)
12. Heung, N.M., Abdulmotaleb, E.S., Geun, S.J.: Collaborative error-reflected models for cold-start recommender systems. Decision Support Systems, vol. 51, no. 3, pp. 519-531 (2011)
13. Lang, K.: NewsWeeder: learning to filter netnews. Proceedings 12th International Conference on Machine Learning, pp. 331–339 (1995)
14. Herlocker, J.L., Konstan, J.A., Riedl, J.T., Terveen, L.G.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, vol. 22, no. 1 pp. 5–53 (2004)
15. Bobadilla, J., Hernando, A., Ortega, F., Bernal, J.: A framework for collaborative filtering recommender systems. Expert Systems with Applications, vol. 38, no. 12, pp. 14609–14623 (2011)
16. Antonopoulus, N., Salter, J.: CinemaScreen recommender agent: combiningcollaborative and content-based filtering. IEEE Intelligent Systems, pp. 35–41 (2006)
17. Gao, L.Q., Li, C.: Hybrid personalized recommended model based on geneticalgorithm. International Conference on Wireless communications, Networking and Mobile Computing, pp. 9215–9218 (2008)
18. Martinez, L., Perez, L.G., Barranco, M.J.: Incomplete preference relations to smooth out the cold-start in collaborative recommender systems. Proceedings of the 28th North American Fuzzy Information Processing Society Annual Conference, pp. 1–6 (2009)
19. Chen, T., He, L.: Collaborative filtering based on demographic attribute vector. Proceedings of the International Conference on Future Computer and Communication, pp. 225–229 (2009)
20. Saranya, M., Atsuhiro, T.: Hybrid recommender systems using latent features. Proceedings of the International Conference on Advanced Information Networking and Applications Workshops, pp. 661–666 (2009)
21. Bobadilla, J., Serradilla, F., and Bernal, J.: A new collaborative filtering metric that improves the behavior of recommender systems. Knowledge-Based Systems, pp. 520-528 (2010)
22. MovieLens dataset, http://grouplens.org/datasets/movielens/
23. Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. ACM SIGIR Workshop on Recommender Systems: Algorithms and Evaluation (1999)
24. Pazzani, M., Billsus, D.: Learning and revising user profile: the identification of interesting web sites. Machine Learning, pp. 313–331 (1997)
25. Joaquin, D., Naohiro, I.: Memory-based weighted-majority prediction. Proceedings of ACM SIGIR’ 99 Workshop on Recommender Systems: Algorithms and Evaluation (1999)
26. Oh, J., Park, S., Yu, H., Song, M., & Park, S. T.: Novel recommendation based on personal popularity tendency. Data Mining (ICDM), 2011 IEEE 11th International Conference on. IEEE, pp. 507-516 (2011)
27. Koren, Y.: Factor in the neighbors: “scalable and accurate collaborative filtering. ACM Transactions on Knowledge Discovery from Data, pp. 1–24 (2010)
28. Ahn, H.J.: Utilizing popularity characteristics for product recommendation. International Journal of Electronic Commerce, pp. 57–78 (2006)
29. Greco, G., Greco, S., Zumpano, E.: Collaborative filtering supporting web site navigation. AI Communications, pp. 155–166 (2004)
30. Hu, Yi-Chung.: Recommendation using neighborhood methods with preference-relation-based similarity. Information Sciences, pp. 18-30 (2014)
31. Zheng, V. W., Cao, B., Zheng, Y., Xie, X., & Yang, Q.: Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach. AAAI, vol. 10, pp. 236-241 (2010)
32. Jin, R., Chai, J. Y. ＆, Si, L.: An automatic weighting scheme for collaborative filtering. Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 337-344 (2004)
33. Formoso, V., Fernández, D., Cacheda, F., & Carneiro, V.: Using profile expansion techniques to alleviate the new user problem. Information Processing & Management, vol. 49, no. 3, pp. 659-672 (2013)
34. Zhou, K., Yang, S. H., & Zha, H.: Functional matrix factorizations for cold-start recommendation. Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pp. 315-324 (2011)