Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2017

Abstract

Review rating prediction is an important research topic. The problem was approached from either the perspective of recommender systems (RS) or that of sentiment analysis (SA). Recent SA research using deep neural networks (DNNs) has realized the importance of user and product interaction for better interpreting the sentiment of reviews. However, the complexity of DNN models in terms of the scale of parameters is very high, and the performance is not always satisfying especially when user-product interaction is sparse. In this paper, we propose a simple, extensible RS-based model, called Text-driven Latent Factor Model (TLFM), to capture the semantics of reviews, user preferences and product characteristics by jointly optimizing two components, a user-specific LFM and a product-specific LFM, each of which decomposes text into a specific low-dimension representation. Furthermore, we address the cold-start issue by developing a novel Pairwise Rating Comparison strategy (PRC), which utilizes the difference between ratings on common user/product as supplementary information to calibrate parameter estimation. Experiments conducted on IMDB and Yelp datasets validate the advantage of our approach over state-of-the-art baseline methods.

Keywords

Machine Learning. Classification, Machine Learning, Data Mining, Multidisciplinary Topics and Applications, Personalization and User Modeling, Natural Language Processing, Sentiment Analysis and Text Mining

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of 26th International Joint Conference on Artificial Intelligence (IJCAI 2017)

First Page

2744

Last Page

2750

Identifier

10.24963/ijcai.2017/382

Publisher

AAAI Press

City or Country

Melbourne, Australia

Additional URL

https://doi.org/10.24963/ijcai.2017/382

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