文章基本信息

标题：Embedded Learning For Leveraging Multi-Aspect In Rating Prediction Of Personalized Recommendation
本地全文：下载
作者：Nurkhairizan Khairudin ; Nurfadhlina Mohd Sharef ; Shahrul Azman Mohd Noah 等
期刊名称：Malaysian Journal of Computer Science
印刷版ISSN：0127-9084
出版年度：2018
卷号：31
期号：5
出版社：University of Malaya * Faculty of Computer Science and Information Technology
摘要：Collaborative filtering that relies on overall ratings has been widely accepted due to the ability to generate satisfactory recommendations. However, the most challenging difficulty of this approach is the lack of sufficient ratings or the socalled data sparsity. Moreover, sometimes these ratings alone are not sufficient to precisely understand users' specific behaviours. A user may show his/her overall preferences on an item through the overall ratings but at the same time, they may not satisfy with certain aspects of the item. This situation happened due to the emphasis on aspects that may be different among users and will effect a user's final decisions. Therefore, in this paper, we proposed a model called Neural Network model for MultiAspect with Strong Correlation (NNMASC) that utilize the significance of aspect’s correlation to enhance the predictive accuracy of personalized recommendation. We integrate the user, item, aspects and overall ratings via embedding features by utilizing the available multiaspect ratings from hotel reviews dataset. NNMASC adopts a feedforward neural network with back propagation algorithm to make rating prediction. The experimental result using MAE shows that the proposed model has significantly outperformed the traditional models and the stateoftheart approaches in terms of prediction accuracy.
关键词：recommender system; collaborative filtering; multiaspect; neural network; correlation strength