Optimization And Implementation Of Item-based Collaborative Filtering Algorithm Based on Attributes and Penalty Factors

DOI

10.2991/aiea-16.2016.8How to use a DOI?

Keywords

Collaborative Filtering; Attribute similarity; Penalty factor; Recommended system.

Abstract

A new item-based collaborative filtering algorithm based on attribute similarity and Penalty was proposed by analyzing the drawbacks of traditional item-based collaborative filtering algorithm according to the similarity between items to select the nearest neighbor[1].The new item-based collaborative filtering algorithm uses the similarity of the item attributes to modify the original item similarity calculation method, and dynamically generates the punish factors according to the item's heat. It comprehensively considers the influence of the item attributes and item heat on the recommendation system, and improves the traditional item similarity measure method. The experimental results on the Movie Lens dataset show that the proposed algorithm can effectively solve the problem of sparse evaluation data and inaccurate recommendation results [2].

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/).

Download article (PDF)

TY  - CONF
AU  - Lukun Zhu
PY  - 2016/11
DA  - 2016/11
TI  - Optimization And Implementation Of Item-based Collaborative Filtering Algorithm Based on Attributes and Penalty Factors
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications
PB  - Atlantis Press
SP  - 44
EP  - 49
SN  - 2352-538X
UR  - https://doi.org/10.2991/aiea-16.2016.8
DO  - 10.2991/aiea-16.2016.8
ID  - Zhu2016/11
ER  -