In the recommendation system, the user's ratings are often affected by the rating context, that is, the user's previous ratings of some items will affect the objectivity of his rating of the current item. Sparse linear method treats user ratings affected by context as the same as other ratings when calculating item similarity. However, this partial ratings cannot objectively reflect the similarity between items. To solve the above problems, this paper proposed a recommendation algorithm combining rating context and item similarity based on sparse linear method. It divided the algorithm into three stages. The first stage used weighted ratings to calculate the item's nearest neighbor for feature selection. In the second stage, it used the rating error weight to reduce the fitting of the ratings affected by the context of the algorithm model, and trained the item similarity matrix. In the third stage, it predicted the ratings according to the user's ratings and the item similarity, and finally sorted the predicted ratings to complete the item recommendation. Experiments were conducted on four datasets of MovieLens, it used mean average precision(MAP), mean reciprocal rank(MRR) and normalized discounted cumulative gain(NDCG) to evaluate the effectiveness of the algorithm. The experimental results show that the fusion rating context will further improve the accuracy of item similarity and thus improve the performance of recommendation.

[2023-04-28] Accepted Paper /doc/2023.02.0057/v1

[2023-10-05] Printed Article /doc/2023.02.0057/v2