A Personalized Recommendation Method Based on Collaborative Filtering Algorithm
International Journal of Business and Economics Research
Volume 8, Issue 5, October 2019, Pages: 297-302
Received: Jun. 28, 2019;
Accepted: Jul. 27, 2019;
Published: Aug. 23, 2019
Views 486 Downloads 53
Liu Hui, School of Management & Economics, Beijing Institute of Technology, Beijing, China
Cui Lixin, School of Management & Economics, Beijing Institute of Technology, Beijing, China
Yao Ting, School of Management & Economics, Beijing Institute of Technology, Beijing, China
Li Rongrong, School of Management & Economics, Beijing Institute of Technology, Beijing, China
Follow on us
Collaborative filtering algorithm is a widely used recommendation algorithm. However, when applied to e-commerce personalized recommendation, it faces the following issues: firstly, how to consider the user's interest changes over time when getting similarity between the users more precise; secondly, how to use social networks to more accurately getting the nearest neighbor of users; and thirdly, how to consider the behavior of users who have the same interests and different ratings in making the predicted rating score of item more accurately; fourthly, how to use the inherent relation between product categories, such as internal relations, while recommending. In order to solve these problems, this paper improves the traditional collaborative filtering algorithm by integrating timing updates, trust relationship, optimization of predicted rating score and structured ideas. To distinguish users' past interest characteristics and their recent ones, by introducing the idea of timing update, this paper regards the user's shopping experience as a set of time periods, considering the influences of the users' interest at different time on the similarity of the users, and the influence of trust relationship between target user and similar users on the establishment of nearest neighbor set. On this basis, faced with the difference of evaluation criteria of different users on the same recommendation item, this study optimizes scoring method of similar users and gets a pre-scoring-based predicted rating score method for target user to recommend item. Furtherly, considering the relationship between the recommended item and other items, this paper also proposes an idea of relative recommending based on recommended item as a secondary recommendation. At the end of this paper, the proposed method is verified on the review dataset in MovieLens which is provided by the College of computer science and engineering of University of Minnesota. The experimental results show that the proposed method has obvious recommendation accuracy compared with the traditional collaborative filtering algorithm.
Collaborative Filtering, Personalized Recommendation, Timing Update, Trust, Relative Recommending
To cite this article
A Personalized Recommendation Method Based on Collaborative Filtering Algorithm, International Journal of Business and Economics Research.
Vol. 8, No. 5,
2019, pp. 297-302.
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Xiang L, “Recommended system practice,” Beijing: People's Posts and Telecommunications Press, 2012.
Shan M, “Design and implementation of e-commerce recommendation system based on personalized recommendation,” Changchun: Jilin University, 2014.
Yuan L, “Research on Collaborative filtering personalized recommendation algorithm based on Clustering,” Wuhan: Huazhong Normal University. 2014.
Wang M J, “Collaborative filtering algorithm based on fuzzy clustering,” Computer Engineering, 2012 38 (24): 50-52.
Chen Q H, “Collaborative filtering recommendation algorithm based on SVD,” Chengdu: Southwest Jiao Tong University, 2012.
Salehi M, “Application of implicit and explicit attribute based collaborative filtering and BIDE for learning resource recommendation,” Data& Knowledge Engineering, 2013, 87 (9): 130-145.
Melville P, Mooney R J, Nagarajan R, “Content-boosted collaborative filtering for improved recommendations,” Proceedings of the National Conference on Artificial Intelligence, Menlo Park, CA; Cambridge; MA; London; AAAI Press; MIT Press; 1999, 2002. 187-192.
Ziegler C N, Lausen G, Schmidt-Thieme L, “Taxonomy-driven computation of product recommendations,” Proceedings of the thirteenth ACM international conference on Information and knowledge management, ACM, 2004, 406-415.
Nasiri M, Minaei B, “Increasing prediction accuracy in collaborative filtering with initialized factor matrices,” Journal of supercomputing, 2016, 72 (6): 2157-2169.
Ma H, King I, Lyu M R, “Effective missing data prediction for collaborative filtering,” Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, ACM, 2007, 39-46.
Greiner R, Su X, Shen B, et al, “Structural extension to logistic regression: Discriminative parameter learning of belief net classifiers,” Machine Learning, 2005, 59 (3): 297-322.
Sarwar B, Karypis G, Konstan J, et al, “Incremental singular value decomposition algorithms for highly scalable recommender systems,” Proceedings of Fifth International Conference on Computer and Information Science, Citeseer, 2002, 27-28.
Herlocker J L, Konstan J A, Borchers A, et al, “An algorithmic framework for performing collaborative filtering,” Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, ACM, 1999, 230-237.
Chee S H S, Han J, Wang K, Rectree, “An efficient collaborative filtering method,” Proceedings of Data Warehousing and Knowledge Discovery, Springer, 2001, 141-151.
Y. Ding, X. Li, “Time weight collaborative filtering,” ACM international conference on Information and knowledge management, Bremen, 2005, 485-492.