Abstract
In a DNN-based recommendation system, the input selection of a model and design of an appropriate input are very important in terms of the accuracy and reflection of complex user preferences. Since the learning of layers by the goal of the model depends on the input, the more closely the input is related to the goal, the less the model needs to learn unnecessary information. In relation to this, the term Drafted-Input, defined in this paper, is input data that have been appropriately selected and processed to meet the goals of the system, and is a subject that is updated while continuously reflecting user preferences along with the learning of model parameters. In this paper, the effects of properly designed and generated inputs on accuracy and usability are verified using the proposed systems. Furthermore, the proposed method and user–item interaction are compared with state-of-the-art systems using simple embedding data as the input, and a model suitable for a practical client–server environment is also proposed.
Highlights
Based Recommendation SystemRecently, various studies related to recommendation systems have been actively conducted
The first is methods to solve the fundamental problems of the recommendation system such as the first rater, the cold start problem, overspecialization, and protection of user privacy [1,2,3], and the second is the improvement of the accuracy of the recommendation system [4,5,6,7,8,9]
We proposed MovieDIRec and MovieDIRec+ using Drafted-Input defined by us
Summary
Based Recommendation SystemRecently, various studies related to recommendation systems have been actively conducted. The first is methods to solve the fundamental problems of the recommendation system such as the first rater, the cold start problem, overspecialization, and protection of user privacy [1,2,3], and the second is the improvement of the accuracy of the recommendation system [4,5,6,7,8,9]. Most of the methods of recent research are methods of inferring preference trends using similar users, such as collaborative filtering, based on vectors embedding user–item interaction. In this case, the model learns the user–item preference distribution. The study mainly solves the cold-start problem or the firstrater problem and is introduced as a solution with improved accuracy compared to pure content-based filtering
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