Application of Using Simulated Annealing to Combine Clustering with Collaborative Filtering for Item Recommendation
Zhiming Feng, Yidan Su
Available Online February 2013.
- https://doi.org/10.2991/isccca.2013.185How to use a DOI?
- item-item collaborative filtering, k-means clustering, simulated annealing, recommender system, recommendation algorithm
- Item-item collaborative filtering was widely used in item recommender system because of good recommend effects. However when facing a large amount of items, there would be performance reduction, because of building a very large item comparison dataset in order to find the similar item. K-means cluster had a very good effect in classification and a good performance even though the dataset being processed is very large. But the cold start was a problem to k-means and we must do some extra work to use it in item recommendation. By using the simulated annealing theory to combine the two methods to fixed the problems of the two methods mentioned above and take use of their advantages for better recommendation effect and performance. The experimental results show that, using simulated annealing to combine the clustering and collaborative filtering in item recommendation system can get more stable recommendation results of better quality.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Zhiming Feng AU - Yidan Su PY - 2013/02 DA - 2013/02 TI - Application of Using Simulated Annealing to Combine Clustering with Collaborative Filtering for Item Recommendation BT - Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation (ISCCCA 2013) PB - Atlantis Press SP - 737 EP - 740 SN - 1951-6851 UR - https://doi.org/10.2991/isccca.2013.185 DO - https://doi.org/10.2991/isccca.2013.185 ID - Feng2013/02 ER -