An Improved Collaborative Filtering Algorithm Based on Multi-Context Awareness

DOI

10.2991/iccmcee-15.2015.28How to use a DOI?

Keywords

Context-Aware; Collaborative Filtering; Recommender Systems

Abstract

In the research field of recommender systems, people tend to focus on mining relationship between users and items, but less take the additional contextual information into consideration. To solve the problem, we put forward an improved collaborative filtering algorithm based on multiple contexts (MCCF). The algorithm utilizes AHP to calculate the weight vector of context features, then transform contextual information into SimHash sequence based on the weight vector to calculate context similarity. Use the composite similarity which compounds the user preference and the context to structure the target user’s nearest neighbor set. Finally, integrate the composite similarity into the traditional collaborative filtering algorithm to predict preferences and recommend items. Experiments show that MCCF is superior to the traditional ones and single context ones in both performance and recommendation accuracy.

Copyright

© 2015, 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  - Yuan Bai
AU  - Yu Fang
PY  - 2015/11
DA  - 2015/11
TI  - An Improved Collaborative Filtering Algorithm Based on Multi-Context Awareness
BT  - Proceedings of the 2015 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering
PB  - Atlantis Press
SP  - 141
EP  - 149
SN  - 2352-5401
UR  - https://doi.org/10.2991/iccmcee-15.2015.28
DO  - 10.2991/iccmcee-15.2015.28
ID  - Bai2015/11
ER  -