A Personalized Recommendation Method Based on Collaborative Filtering Algorithm

Liu Hui; Cui Lixin; Yao Ting; Li Rongrong

doi:doi:10.11648/j.ijber.20190805.16

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A Personalized Recommendation Method Based on Collaborative Filtering Algorithm

Liu Hui, Cui Lixin, Yao Ting, Li Rongrong

Published in International Journal of Business and Economics Research (Volume 8, Issue 5)

Received: 28 June 2019 Accepted: 27 July 2019 Published: 23 August 2019

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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.

Published in	International Journal of Business and Economics Research (Volume 8, Issue 5)
DOI	10.11648/j.ijber.20190805.16
Page(s)	297-302
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Collaborative Filtering, Personalized Recommendation, Timing Update, Trust, Relative Recommending

References

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[2]	Shan M, “Design and implementation of e-commerce recommendation system based on personalized recommendation,” Changchun: Jilin University, 2014.
[3]	Yuan L, “Research on Collaborative filtering personalized recommendation algorithm based on Clustering,” Wuhan: Huazhong Normal University. 2014.
[4]	Wang M J, “Collaborative filtering algorithm based on fuzzy clustering,” Computer Engineering, 2012 38 (24): 50-52.
[5]	Chen Q H, “Collaborative filtering recommendation algorithm based on SVD,” Chengdu: Southwest Jiao Tong University, 2012.
[6]	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.
[7]	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.
[8]	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.
[9]	Nasiri M, Minaei B, “Increasing prediction accuracy in collaborative filtering with initialized factor matrices,” Journal of supercomputing, 2016, 72 (6): 2157-2169.
[10]	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.
[11]	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.
[12]	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.
[13]	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.
[14]	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.
[15]	Y. Ding, X. Li, “Time weight collaborative filtering,” ACM international conference on Information and knowledge management, Bremen, 2005, 485-492.

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APA Style

Liu Hui, Cui Lixin, Yao Ting, Li Rongrong. (2019). A Personalized Recommendation Method Based on Collaborative Filtering Algorithm. International Journal of Business and Economics Research, 8(5), 297-302. https://doi.org/10.11648/j.ijber.20190805.16

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ACS Style

Liu Hui; Cui Lixin; Yao Ting; Li Rongrong. A Personalized Recommendation Method Based on Collaborative Filtering Algorithm. Int. J. Bus. Econ. Res. 2019, 8(5), 297-302. doi: 10.11648/j.ijber.20190805.16

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AMA Style

Liu Hui, Cui Lixin, Yao Ting, Li Rongrong. A Personalized Recommendation Method Based on Collaborative Filtering Algorithm. Int J Bus Econ Res. 2019;8(5):297-302. doi: 10.11648/j.ijber.20190805.16

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@article{10.11648/j.ijber.20190805.16,
  author = {Liu Hui and Cui Lixin and Yao Ting and Li Rongrong},
  title = {A Personalized Recommendation Method Based on Collaborative Filtering Algorithm},
  journal = {International Journal of Business and Economics Research},
  volume = {8},
  number = {5},
  pages = {297-302},
  doi = {10.11648/j.ijber.20190805.16},
  url = {https://doi.org/10.11648/j.ijber.20190805.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijber.20190805.16},
  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.},
 year = {2019}
}

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TY  - JOUR
T1  - A Personalized Recommendation Method Based on Collaborative Filtering Algorithm
AU  - Liu Hui
AU  - Cui Lixin
AU  - Yao Ting
AU  - Li Rongrong
Y1  - 2019/08/23
PY  - 2019
N1  - https://doi.org/10.11648/j.ijber.20190805.16
DO  - 10.11648/j.ijber.20190805.16
T2  - International Journal of Business and Economics Research
JF  - International Journal of Business and Economics Research
JO  - International Journal of Business and Economics Research
SP  - 297
EP  - 302
PB  - Science Publishing Group
SN  - 2328-756X
UR  - https://doi.org/10.11648/j.ijber.20190805.16
AB  - 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.
VL  - 8
IS  - 5
ER  -

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Author Information

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

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APA Style

Liu Hui, Cui Lixin, Yao Ting, Li Rongrong. (2019). A Personalized Recommendation Method Based on Collaborative Filtering Algorithm. International Journal of Business and Economics Research, 8(5), 297-302. https://doi.org/10.11648/j.ijber.20190805.16

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ACS Style

Liu Hui; Cui Lixin; Yao Ting; Li Rongrong. A Personalized Recommendation Method Based on Collaborative Filtering Algorithm. Int. J. Bus. Econ. Res. 2019, 8(5), 297-302. doi: 10.11648/j.ijber.20190805.16

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AMA Style

Liu Hui, Cui Lixin, Yao Ting, Li Rongrong. A Personalized Recommendation Method Based on Collaborative Filtering Algorithm. Int J Bus Econ Res. 2019;8(5):297-302. doi: 10.11648/j.ijber.20190805.16

Copy | Download

@article{10.11648/j.ijber.20190805.16,
  author = {Liu Hui and Cui Lixin and Yao Ting and Li Rongrong},
  title = {A Personalized Recommendation Method Based on Collaborative Filtering Algorithm},
  journal = {International Journal of Business and Economics Research},
  volume = {8},
  number = {5},
  pages = {297-302},
  doi = {10.11648/j.ijber.20190805.16},
  url = {https://doi.org/10.11648/j.ijber.20190805.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijber.20190805.16},
  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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - A Personalized Recommendation Method Based on Collaborative Filtering Algorithm
AU  - Liu Hui
AU  - Cui Lixin
AU  - Yao Ting
AU  - Li Rongrong
Y1  - 2019/08/23
PY  - 2019
N1  - https://doi.org/10.11648/j.ijber.20190805.16
DO  - 10.11648/j.ijber.20190805.16
T2  - International Journal of Business and Economics Research
JF  - International Journal of Business and Economics Research
JO  - International Journal of Business and Economics Research
SP  - 297
EP  - 302
PB  - Science Publishing Group
SN  - 2328-756X
UR  - https://doi.org/10.11648/j.ijber.20190805.16
AB  - 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.
VL  - 8
IS  - 5
ER  -

Copy | Download