A Combinative Similarity Computing Measure for Collaborative Filtering

DOI

10.2991/iccsee.2013.482How to use a DOI?

Keywords

user-based collaborative filtering, similarity method, item’s account users co-rated,

Abstract

Similarity method is the key of the user-based collaborative filtering recommend algorithm. The traditional similarity measures, which cosine similarity, adjusted cosine similarity and Pearson correlation similarity are included, have some advantages such as simple, easy and fast, but with the sparse dataset they may lead to bad recommendation quality. In this article, we first research how the recommendation qualities using the three similarity methods respectively change with the different sparse datasets, and then propose a combinative similarity measure considering the account of items users co-rated. Compared with the three algorithms, our method shows its satisfactory performance with the same computation complexity.

Copyright

© 2013, 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  - Lin Guo
AU  - Qinke Peng
PY  - 2013/03
DA  - 2013/03
TI  - A Combinative Similarity Computing Measure for Collaborative Filtering
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 1921
EP  - 1924
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.482
DO  - 10.2991/iccsee.2013.482
ID  - Guo2013/03
ER  -