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http://dx.doi.org/10.3745/JIPS.02.0159

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)
Publication Information
Journal of Information Processing Systems / v.17, no.4, 2021 , pp. 707-720 More about this Journal
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
Bidirectional Long Short-Term Memory (BiLSTM); Convolutional Neural Network (CNN); Multi-Criteria Recommendation System; Recommendation System;
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