Joint Inter-word and Inter-sentence Multi-relationship Modeling for Review-basedRecommendation Algorithm
Year: 2025
Type: article
Abstract: Reviews,a prevalent form of auxiliary information,directly reflect user preferences and item characteristics,extensively utilized by researchers to enhance the predictive accuracy of recommendation algorithms.However,the current review recommendation algorithm still has shortcomings,which are mainly reflected in the fact that the existing model ignores the modeling of multi-granularity feature extraction of review text and the relational interactive learning of user preferences and item attributes,a pair of heterogeneous features.This oversight leads to insufficient extraction of review information,compromising model accuracy.Thus,joint inter-word and inter-sentence multi-relationship modeling for review-based recommendation algorithm(MR4R) is introduced in the study.Firstly,multi-relational modeling strategy is adopted to analyze inter-word and inter-sentence relationships in review texts to extract layered feature information,thereby enriching the model’s grasp of user preferences and refining item attribute representations.The fusion and prediction layer is designed to optimize the correlation mining process between user preferences and item attributes,and the score prediction is carried out by high order nonlinear calculation.The proposed model is compared with seven current mainstream recommendation algorithms on four distinct datasets.The results demonstrate that the recommendation algorithm,which incorporates multi-relational modeling between words and sentences,effectively extracts information embedded in reviews,significantly enhancing average recommendation accuracy and exhibiting superior performance.
Author: DENG Ceyu, LI Duantengchuan, HU Yiren, WANG Xiaoguang, LI Zhifei
Institutions: Wuhan University, Hubei University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7084070732
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