Integrating Reviews for Item Recommendation Using an Adaptive Hierarchical Attention-Enhanced Gated Network

PROJECT TITLE :

Adaptive Hierarchical Attention-Enhanced Gated Network Integrating Reviews for Item Recommendation

ABSTRACT:

There have been a number of very successful studies that have focused on integrating ratings and reviews in order to improve the performance of recommendations. Nevertheless, these works continue to suffer from a number of flaws: (1) The significance of dynamically integrating review and interaction data features is frequently overlooked, despite the fact that treating these fusion features equally may lead to an insufficient comprehension of user preferences. (2) In order to model the local semantic information of words, some forms of soft attention modeling methods are utilized. Because the features that are captured in this way may contain information that is not relevant, the attention map that is generated is neither discriminatory nor detailed. In this article, we propose a novel adaptive hierarchical attention-enhanced gated network that integrates reviews for the purpose of item recommendation. We refer to this network as AHAG. AHAG is an integrated framework that makes it possible to uncover the users' covert goals through the dynamic incorporation of user feedback. Specifically, we design a gated network to dynamically fuse the extracted features and select the features that are most relevant to user preferences. This network also helps us select the features that are most accurate. We introduce a hierarchical attention mechanism to learn important semantic information features and the dynamic interaction of these features in order to capture distinguishing fine-grained features. This allows us to capture distinguishing fine-grained features. In addition to this, the high-order non-linear interaction of neural factorization machines is exploited in order to derive the rating prediction. Experiments conducted on seven datasets taken from the real world demonstrate that the proposed AHAG performs noticeably better than methods that are considered to be state-of-the-art. In addition, the attention mechanism has the capability of highlighting the pertinent information in reviews, which improves the interpretability of the task of making recommendations.

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