Collaborative filtering recommendation algorithm based on nearest neighbor clustering

With the increasing number of users and items in recommender systems, designing a scalable algorithm becomes a big challenge for recommendation systems. However, many recommendation algorithms and the improved algorithms proposed thus far have focused on improving recommendation quality, resulting in shortcomings such as lower recommendation efficiency and running time consumption as the system increases in scale. To address the problem of scalability, a collaborative filtering recommendation algorithm based on nearest neighbor clustering was proposed. Firstly, the k-means algorithm was utilized to place similar scores into the same cluster, which was used to build the user clustering model. Then, it picked out the active users’ nearest neighbor clusters from the clustering model and treats them as a retrieval space. Finally, the nearest neighbors of an active user are found according to the retrieval space, and the recommendation to the active user was given. Experimental results show that the algorithm proposed in this paper not only significantly improves the response speed of the recommendation system online but also maintains a high accuracy.