Collaborative Filtering Algorithm Based on Random Walk with Choice

DOI

10.2991/sekeie-14.2014.45How to use a DOI?

Keywords

Recommender systems; Collaborative filtering; Random walk with choice

Abstract

A brief review of the past researches on CF shows that methods for calculating users’ similarities are almost Pearson Correlation or (adjusted) Cosine Similarity. This leads to same recommendations for different users because popular objects or users often win a heavier weight in the process of recommendation. Moreover, it has been increasingly recognized that the gains of the recommendation accuracy are often accompanied by the losses of the diversity. In order to walk out of the accuracy-diversity dilemma, we propose a new method named collaborative filtering based on random walk with choice which replaces the traditional Pearson Correlation or (adjusted) Cosine Similarity with random walk with choice for calculating users’ similarities. Results show that our approach significantly outperforms the ordinary user-based collaborative filtering method in diversity without lowing recommendation accuracy.

Copyright

© 2014, 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  - Chuanmin Mi
AU  - Xiaofei Shan
AU  - Jing Ma
AU  - Xin Zhang
PY  - 2014/03
DA  - 2014/03
TI  - Collaborative Filtering Algorithm Based on Random Walk with Choice
BT  - Proceedings of the 2nd International Conference on Software Engineering, Knowledge Engineering and Information Engineering (SEKEIE 2014)
PB  - Atlantis Press
SP  - 192
EP  - 196
SN  - 1951-6851
UR  - https://doi.org/10.2991/sekeie-14.2014.45
DO  - 10.2991/sekeie-14.2014.45
ID  - Mi2014/03
ER  -