Hierarchical Text Interaction for Rating Prediction

by   Jiahui Wen, et al.

Traditional recommender systems encounter several challenges such as data sparsity and unexplained recommendation. To address these challenges, many works propose to exploit semantic information from review data. However, these methods have two major limitations in terms of the way to model textual features and capture textual interaction. For textual modeling, they simply concatenate all the reviews of a user/item into a single review. However, feature extraction at word/phrase level can violate the meaning of the original reviews. As for textual interaction, they defer the interactions to the prediction layer, making them fail to capture complex correlations between users and items. To address those limitations, we propose a novel Hierarchical Text Interaction model(HTI) for rating prediction. In HTI, we propose to model low-level word semantics and high-level review representations hierarchically. The hierarchy allows us to exploit textual features at different granularities. To further capture complex user-item interactions, we propose to exploit semantic correlations between each user-item pair at different hierarchies. At word level, we propose an attention mechanism specialized to each user-item pair, and capture the important words for representing each review. At review level, we mutually propagate textual features between the user and item, and capture the informative reviews. The aggregated review representations are integrated into a collaborative filtering framework for rating prediction. Experiments on five real-world datasets demonstrate that HTI outperforms state-of-the-art models by a large margin. Further case studies provide a deep insight into HTI's ability to capture semantic correlations at different levels of granularities for rating prediction.


page 1

page 3

page 7

page 20

page 24

page 25

page 26

page 27


A Context-Aware User-Item Representation Learning for Item Recommendation

Both reviews and user-item interactions (i.e., rating scores) have been ...

Boosting the Rating Prediction with Click Data and Textual Contents

Matrix factorization (MF) is one of the most efficient methods for ratin...

KNNs of Semantic Encodings for Rating Prediction

This paper explores a novel application of textual semantic similarity t...

A Review-Driven Neural Model for Sequential Recommendation

Writing review for a purchased item is a unique channel to express a use...

Convolutional Quantum-Like Language Model with Mutual-Attention for Product Rating Prediction

Recommender systems are designed to help mitigate information overload u...

Knowledge-aware Collaborative Filtering with Pre-trained Language Model for Personalized Review-based Rating Prediction

Personalized review-based rating prediction aims at leveraging existing ...

TADO: Time-varying Attention with Dual-Optimizer Model

The review-based recommender systems are commonly utilized to measure us...

Please sign up or login with your details

Forgot password? Click here to reset