Recommendation Model Based on Collaborative Filtering Recommendation Algorithm

DOI

10.2991/mmme-16.2016.16How to use a DOI?

Keywords

Collaborative Filtering; Recommendation; User Rating Scale; Welcome Degree Valuation; Sparse Matrix Evaluation

Abstract

There are problems concern the current recommendation model such as the information recommended is not inaccurate enough. This paper presents a collaborative filtering algorithm based on K-means algorithm. Firstly, we analyzed the similarity calculation method of collaborative filtering recommendation algorithm, then we proposed a valuation formula based on user rating scale and information popularity to assign value for ungraded items at sparse ratings matrices to improve the scoring matrix density, increase the accuracy of similarity calculation, and build the recommendation model. Simulation results show that the proposed collaborative filtering recommendation algorithm based on K-means has higher prediction accuracy and classification accuracy than traditional collaborative filtering algorithm.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jun Huang
PY  - 2016/10
DA  - 2016/10
TI  - Recommendation Model Based on Collaborative Filtering Recommendation Algorithm
BT  - Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering
PB  - Atlantis Press
SP  - 67
EP  - 70
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmme-16.2016.16
DO  - 10.2991/mmme-16.2016.16
ID  - Huang2016/10
ER  -