An improvement of video recommender similarity measurement model

DOI

10.2991/amcce-15.2015.122How to use a DOI?

Keywords

Recommender System; Collaborative Filtering; Item Attribute; Similarity

Abstract

Collaborative recommender systems have succeeded in capturing the similarity between users and items based on ratings. However, they have rarely considered about the available information of the multimedia such as categories, delivery time and so on. Such information are valuable and feasible to solve rating bias problems in recommender systems. We found that user described their preferences directly to the item rating data is not comprehensive. In this paper, we design IBHF (Item-attribute Based Hybrid Filtering) based on movie features of the multimedia information. In the IBHF, we provide recommendation service by new Pearson method which is a similarity measure technique used to integrate movie attributes into the Item-based collaborative filtering (IBCF) framework in hopes of achieving better performance. The experiment prove that this method can make the items recommendation more ideal, and also provides a solution to solve the cold start problem to different recommendation items.

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  - Zhenrong Deng
AU  - Xi Zhang
AU  - Xing Deng
AU  - Liang Xu
AU  - Wenming Huang
PY  - 2015/04
DA  - 2015/04
TI  - An improvement of video recommender similarity measurement model
BT  - Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering
PB  - Atlantis Press
SP  - 1388
EP  - 1393
SN  - 1951-6851
UR  - https://doi.org/10.2991/amcce-15.2015.122
DO  - 10.2991/amcce-15.2015.122
ID  - Deng2015/04
ER  -