Application of Using Simulated Annealing to Combine Clustering with Collaborative Filtering for Item Recommendation
Zishuo Feng, Yi Dan Su · Applied Mechanics and Materials · 2013
tem-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.