Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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The Method for Generating Recommended Candidates through Prediction of Multi-Criteria Ratings Using CNN-BiLSTM

  • Kim, Jinah (Dept. of Computer Science and Engineering, Hoseo University) ;
  • Park, Junhee (Dept. of Computer Science and Engineering, Hoseo University) ;
  • Shin, Minchan (Dept. of Computer Science and Engineering, Hoseo University) ;
  • Lee, Jihoon (Dept. of Computer Science and Engineering, Hoseo University) ;
  • Moon, Nammee (Dept. of Computer Science and Engineering, Hoseo University)
  • Received : 2020.03.19
  • Accepted : 2020.10.04
  • Published : 2021.08.31
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Abstract

To improve the accuracy of the recommendation system, multi-criteria recommendation systems have been widely researched. However, it is highly complicated to extract the preferred features of users and items from the data. To this end, subjective indicators, which indicate a user's priorities for personalized recommendations, should be derived. In this study, we propose a method for generating recommendation candidates by predicting multi-criteria ratings from reviews and using them to derive user priorities. Using a deep learning model based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), multi-criteria prediction ratings were derived from reviews. These ratings were then aggregated to form a linear regression model to predict the overall rating. This model not only predicts the overall rating but also uses the training weights from the layers of the model as the user's priority. Based on this, a new score matrix for recommendation is derived by calculating the similarity between the user and the item according to the criteria, and an item suitable for the user is proposed. The experiment was conducted by collecting the actual "TripAdvisor" dataset. For performance evaluation, the proposed method was compared with a general recommendation system based on singular value decomposition. The results of the experiments demonstrate the high performance of the proposed method.

Keywords

Acknowledgement

This research is supported by Ministry of Culture, Sports and Tourism (MCST) and Korea Creative Content Agency (KOCCA) in the Culture Technology (CT) Research & Development Program 2020 (No. R2018020083).

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