Abstract
This paper explores and studies recommendation technologies based on content filtering and user collaborative filtering and proposes a hybrid recommendation algorithm based on content and user collaborative filtering. This method not only makes use of the advantages of content filtering but also can carry out similarity matching filtering for all items, especially when the items are not evaluated by any user, which can be filtered out and recommended to users, thus avoiding the problem of early level. At the same time, this method also takes advantage of the advantages of collaborative filtering. When the number of users and evaluation levels are large, the user rating data matrix of collaborative filtering prediction will become relatively dense, which can reduce the sparsity of the matrix and make collaborative filtering more accurate. In this way, the system performance will be greatly improved through the integration of the two. On the basis of the improved collaborative filtering algorithm, a hybrid algorithm based on content and improved collaborative filtering was proposed. By combining user rating with item features, a user feature rating matrix was established to replace the traditional user-item rating matrix. K-means clustering was performed on the user set and recommendations were made. The improved algorithm can solve the problem of data sparsity of traditional collaborative filtering algorithm. At the same time, for new projects, it can also predict users who may be interested in new projects according to the matching of project characteristics and user characteristics scoring matrix and generate push list, which effectively solve the problem of new projects in “cold start.” The experimental results show that the improved algorithm in this paper plays a significant role in solving the speed bottleneck problems of data sparsity, cold start, and online recommendation and can ensure a better recommendation quality.
Highlights
Related WorkThere are quite a few e-commerce systems using recommendation technology to improve the revenue of enterprises
Academic Editor: Yi-Zhang Jiang is paper explores and studies recommendation technologies based on content filtering and user collaborative filtering and proposes a hybrid recommendation algorithm based on content and user collaborative filtering. is method makes use of the advantages of content filtering and can carry out similarity matching filtering for all items, especially when the items are not evaluated by any user, which can be filtered out and recommended to users, avoiding the problem of early level
For new projects, it can predict users who may be interested in new projects according to the matching of project characteristics and user characteristics scoring matrix and generate push list, which effectively solve the problem of new projects in “cold start.” e experimental results show that the improved algorithm in this paper plays a significant role in solving the speed bottleneck problems of data sparsity, cold start, and online recommendation and can ensure a better recommendation quality
Summary
There are quite a few e-commerce systems using recommendation technology to improve the revenue of enterprises. (1) A method to optimize the user similarity calculation formula by using project heat was proposed (2) In order to present users’ preferences more stereoscopic, the table-oriented feature extraction is carried out in the content-based recommendation algorithm, and the square-one method for calculating users’ similarity using the interest model is presented (3) According to the characteristics of the algorithm in this paper, a method to derive the weight coefficients of different features by using variance is proposed e purpose of the content-based recommendation algorithm is to effectively filter out the third category of users whose interests are different from those of the target users, and the work required in this process generally includes three steps. After extracting item features and establishing the interest model for users by using item features and scoring matrix, the step is to consider how to use the user interest model to calculate the similarity between users. e calculation of similarity can often be transformed into distance visually
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