Collaborative Filtering Recommendation Algorithm Based on Improved Similarity Computing
Aili Liu, Baoan Li
Available Online December 2015.
- https://doi.org/10.2991/icmmcce-15.2015.262How to use a DOI?
- Personalized Recommendation; Collaborative Filtering; MAE (Mean Absolute Error); User Characteristic; Item Attribute
- At presently the most widely used algorithm is collaborative filtering in the Personalized Recommendation Systems for E-Commerce. Aiming at the problem that the recommendation is not accurate due to the data sparsity, a collaborative filtering algorithm based on user characteristics and item attributes preference was proposed in this study. It obtained the nearest neighbor users and similar items by analyzing the user characteristics, item attributes and the data of user's historical scores, and then computing the similarity between the two users based on the user-based collaborative filtering algorithm. It gave an algorithm which has the lower value of MAE and may improve the accuracy of the recommendation services.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Aili Liu AU - Baoan Li PY - 2015/12 DA - 2015/12 TI - Collaborative Filtering Recommendation Algorithm Based on Improved Similarity Computing BT - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015 PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/icmmcce-15.2015.262 DO - https://doi.org/10.2991/icmmcce-15.2015.262 ID - Liu2015/12 ER -