An improvement of video recommender similarity measurement model
Zhenrong Deng, Xi Zhang, Xing Deng, Liang Xu, Wenming Huang
Available Online April 2015.
- https://doi.org/10.2991/amcce-15.2015.122How to use a DOI?
- Recommender System; Collaborative Filtering; Item Attribute; Similarity
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 2015 International Conference on Automation, Mechanical Control and Computational Engineering PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/amcce-15.2015.122 DO - https://doi.org/10.2991/amcce-15.2015.122 ID - Deng2015/04 ER -