Volume 8, Issue 5, October 2019, Page: 297-302
A Personalized Recommendation Method Based on Collaborative Filtering Algorithm
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
Received: Jun. 28, 2019;       Accepted: Jul. 27, 2019;       Published: Aug. 23, 2019
DOI: 10.11648/j.ijber.20190805.16      View  38      Downloads  9
Abstract
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.
Keywords
Collaborative Filtering, Personalized Recommendation, Timing Update, Trust, Relative Recommending
To cite this article
Liu Hui, Cui Lixin, Yao Ting, Li Rongrong, A Personalized Recommendation Method Based on Collaborative Filtering Algorithm, International Journal of Business and Economics Research. Vol. 8, No. 5, 2019, pp. 297-302. doi: 10.11648/j.ijber.20190805.16
Copyright
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.
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