Abstract
For user-based collaborative filtering, the similarity methods used to calculate the target user’s neighbors are very important. More similar neighbors lead to better recommendations and more accurate results. There are a lot of similarity methods till now, but there is still a room for improvement, especially when the data is sparse. It is well known that sparse data can easily lead to cold start problems. The performances of most traditional methods are disappointing under cold start conditions. In order to get a better performance under the cold start conditions, we proposed a new similarity method based on the idea that users with similar interests in the past will show similar tastes in the future. While considering similarities between items and rational inferences, the proposed method focuses on how to utilize more ratings information. At the same time, in order to reduce the time spent on calculations and reduce the impact of excessive ratings information, we have limited the range of items neighbors through experiments. Besides, the proportion of co-rate items to personally rated items is different from each user, base on which the asymmetric factor is considered. Experiments on the dataset MovieLens prove that the proposed method outperforms state-of-the-art methods.
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References
Putra, A.A., Mahendra, R., Budi, I., Munajat, Q.: Two-steps graph-based collaborative filtering using user and item similarities: case study of E-commerce recommender systems. In: International Conference on Data and Software Engineering, pp. 1–6 (2017)
Aditya, P.H., Budi, I., Munajat, Q.: A comparative analysis of memory-based and model-based collaborative filtering on the implementation of recommender system for E-commerce in Indonesia: a case study PT X. In: International Conference on Advanced Computer Science and Information Systems, pp. 303–308. IEEE, Malang (2016)
Lee, S.K., Cho, Y.H., Kim, S.H.: Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations. Inf. Sci. 180(11), 2142–2155 (2010)
Naser, I., Pagare, R., Wathap, N.K., Pingale, V.: Hybrid music recommendation system: enhanced collaborative filtering using context and interest based approach. In: Annual IEEE India Conference, pp. 1–11. IEEE, Pune (2014)
Song, C.: Application of an improved collaborative filtering method on recommending books in college libraries. Libr. Inf. Serv. (2016)
Ng, Y.-K.: Recommending books for children based on the collaborative and content-based filtering approaches. In: Gervasi, O., et al. (eds.) ICCSA 2016. LNCS, vol. 9789, pp. 302–317. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42089-9_22
Dong, Y., Liu, S., Chai, J.: Research of hybrid collaborative filtering algorithm based on news recommendation. In: 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, pp. 898–902. IEEE, Datong (2016)
Mathew, P., Kuriakose, B., Hegde, V.: Book recommendation system through content based and collaborative filtering method. In: 2016 International Conference on Data Mining and Advanced Computing, pp. 47–52. IEEE, Ernakulam (2016)
Saranya, K.G., Sadasivam, G.S.: Personalized news article recommendation with novelty using collaborative filtering based rough set theory. Mob. Netw. Appl. 22(1), 1–11 (2017)
Do, P., Nguyen, K., Vu, T.N., Dung, T.N., Le, T.D.: Integrating knowledge-based reasoning algorithms and collaborative filtering into e-learning material recommendation system. In: Dang, T.K., et al. (eds.) FDSE 2017. LNCS, vol. 10646, pp. 419–432. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70004-5_30
Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press, New York (2010)
Bobadilla, J., Ortega, F., Hernando, A.: Recommender systems survey. Knowl.-Based Syst. 46(1), 109–132 (2013)
Yang, J.M., Li, K.F.: Recommendation based on rational inferences in collaborative filtering. Knowl.-Based Syst. 22(1), 105–114 (2009)
Wei, J., He, J., Chen, K., Zhou, Y., Tang, Z.: Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst. Appl. 69, 29–39 (2017)
Bobadilla, J., Ortega, F., Hernando, A., Bernal, J.: A collaborative filtering approach to mitigate the new user cold start problem. Knowl.-Based Syst. 26, 225–238 (2012)
Grolman, E., Bar, A., Shapira, B., Rokach, L., Dayan, A.: Utilizing transfer learning for in-domain collaborative filtering. Know.-Based Syst. 107(C), 70–82 (2016)
Li, B., Zhu, X., Li, R., Zhang, C.: Rating knowledge sharing in cross-domain collaborative filtering. IEEE Trans. Cybern. 45(5), 1068–1082 (2015)
Pan, W., Liu, M., Ming, Z.: Transfer learning for heterogeneous one-class collaborative filtering. IEEE Intell. Syst. 31(4), 43–49 (2016)
Nguyen, V.D., Sriboonchitta, S., Huynh, V.N.: Using community preference for overcoming sparsity and cold-start problems in collaborative filtering system offering soft ratings. Electron. Commer. Res. Appl. 26, 101–108 (2017)
Chen, Z., Shen, L., Li, F., You, D.: Your neighbors alleviate cold-start: on geographical neighborhood influence to collaborative web service QoS prediction. Knowl.-Based Syst. 138, 188–201 (2017)
Ahn, H.J.: A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf. Sci. 178(1), 37–51 (2008)
Liu, H., Hu, Z., Mian, A., Tian, H., Zhu, X.: A new user similarity model to improve the accuracy of collaborative filtering. Knowl.-Based Syst. 56(3), 156–166 (2014)
Wang, Y., Deng, J., Gao, J., Zhang, P.: A hybrid user similarity model for collaborative filtering. Inf. Sci. 418–419, 102–118 (2017)
Millan, M., Trujillo, M., Ortiz, E.: A collaborative recommender system based on asymmetric user similarity. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds.) IDEAL 2007. LNCS, vol. 4881, pp. 663–672. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-77226-2_67
Pirasteh, P., Jung, Jason J., Hwang, D.: An asymmetric weighting schema for collaborative filtering. In: Camacho, D., Kim, S.-W., Trawiński, B. (eds.) New Trends in Computational Collective Intelligence. SCI, vol. 572, pp. 77–82. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-10774-5_7
Liu, Z., Shihua, O.U., Hang, S.: Collaborative filtering recommendation algorithm based on asymmetric weighted user similarity. J. Chin. Comput. Syst. 38(4), 721–725 (2017)
Cao, B.: Learning bidirectional asymmetric similarity for collaborative filtering via matrix factorization. Data Min. Knowl. Disc. 22(3), 393–418 (2011)
Linden, G.: What is a good recommendation algorithm? https://cacm.acm.org/blogs/blog-cacm/22925-what-is-a-good-recommendation-algorithm/fulltext
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Wang, D., Wang, C. (2018). A New Asymmetric User Similarity Model Based on Rational Inference for Collaborative Filtering to Alleviate Cold Start Problem. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_43
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