Cold start project recommendation method based on embedded feature selection

Abstract

The invention discloses a cold start project recommendation method based on embedded feature selection. The method comprises the steps of obtaining a user set, a project set and a feature set; generating a user project interaction matrix and a project feature matrix; establishing an optimization model of user project prediction based on the embedded feature selection; carrying out parameter optimization on the optimization model by utilizing a supervised learning method; substituting the optimized parameters into the prediction model, and calculating a prediction rating score of the project; and recommending the project according to the predicted rating score. According to the method provided by the invention, the individual difference of each user in feature selection is considered, and the existing of common features is considered, the individuation of recommended features is ensured, at the same time, the dimension reduction is realized. In addition, the model is optimized through the regularization of the elastic network and the high-efficiency algorithm based on the soft threshold, so that the method provided by the invention has the stronger superiority compared with other recommendation algorithms.

Description

technical field [0001] The invention belongs to the field of intelligent recommendation, and in particular relates to a cold start item recommendation method based on embedded feature selection. Background technique [0002] The rapid growth of online e-commerce and the dramatic increase in products require systems that can effectively help customers identify products that best meet their personal preferences. Top-N recommender systems fill this gap by providing users with ordered lists of items. Due to the strong utility of top-N recommendations in many real-world scenarios, various efforts have been devoted to providing top-N recommendations in the past decades. The traditional top-N recommendation determines the set of items to be recommended through the historical information of the user's favorite items. However, in many recommendation applications, some new items have been added recently, and the system has no relevant historical information for these items. Recomme...

AI Technical Summary

Problems solved by technology

While these can guarantee high performance for state-of-the-art top-N new item recommendation methods, the performance of these methods drops dramatically when the auxiliary information is high-dimensional

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