SEMAX : Multi-task Learning for Improving Recommendations

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)2305-2314
Journal / PublicationIEEE Access
Volume7
Online published11 Dec 2018
Publication statusPublished - 2019

Abstract

Personalization plays an essential role in recommender systems, in which the key task is to predict personalized ratings of users on new items. Recently, a lot of work investigate deep learning based collaborative filtering techniques to increase the accuracy of rating prediction. However, most exiting works focus on the recommendation task itself. Actually, multi-task learning exploits an inductive transfer mechanism to enhance the generalization performance of the main task by using the domain information contained in other related tasks. Multi-task learning has shown effectiveness in various real-world problems, including regression and machine translation. To the end, this study proposes a new framework, called SEMAX that extends our previous model SEMA via multi-task learning for improving recommendations, in which the recommendation task gets the domain information from the other task. Specifically, in the recommendation task SEMAX learns semantic meanings from texts and temporal dynamics from text sequences for both users and items based on our developed hierarchical and symmetrical recurrent neural networks (RNNs) with the long short-term memory (LSTM). Furthermore, SEMAX exploits the related task that predicts the rating of a text written by a user for an item to reinforce the recommendation task that predicts the rating of the user on the item, because the text can be an important predictor of the rating given by the user to the item. Moreover, SEMAX predicts the rating of a text based on an attention mechanism to choose user-item-specific words so as to generalize the performance of learned word embeddings, user and item representations. Finally, we conduct a comprehensive evaluation for SEMAX using two large-scale real-world review datasets collected from Amazon and Yelp. Experimental results show that SEMAX achieves significantly superior performance compared to other state-of-the-art recommendation techniques.

Research Area(s)

  • Rating prediction, semantic meanings, temporal dynamics, multi-task learning, attention

Citation Format(s)