Du, Jiahua (2020) Advanced Review Helpfulness Modeling. PhD thesis, Victoria University.

Abstract

In recent years, online shopping has gained immense popularity due to its feedback mechanism. By composing online comments, previous buyers share opinions and expe-riences regarding the items that they have purchased. These user-generated reviews, in turn, provide valuable information to potential customers in regards to deciding which products to purchase. The reviews also help vendors understand customer needs and improve product quality. Yet despite these benefits, the unprecedentedly rapid growth of user-generated content has overwhelmed human ability in online review scrutiny. On-line reviews that possess varying content further impedes useful knowledge distillation. The large volume of online reviews that are uneven in quality puts growing pressure on automatic approaches for effective review utilization and informative content prioritiza-tion. Review helpfulness prediction leverages machine learning methods to identify and recommend helpful reviews to customers. In particular, review characteristics form the backbone of helpfulness information acquisition. Prior literature has observed and as-sociated a large body of determinants with review helpfulness. However, these deter-minants heavily rely on the domain knowledge of experts. The selection of and the interaction between the determinants also remain understudied, leaving ample room for exploration. The general lack of systematic experiment protocols among the existing methods further harms the task’s reproducibility, comparability, and generalizability. This thesis aims to automatically model helpfulness information from online user- generated reviews. The thesis proposes effective modeling techniques and novel so-lutions to tackle the aforementioned challenges, with more emphasis on sophisticated feature learning and interaction. The thesis has made the following contributions to standardize the research field and advance the accuracy in helpfulness prediction. 1. A comprehensive survey is conducted to identify frequently used content-based determinants for automatic helpfulness prediction. A computational framework is developed to empirically evaluate the identified features across domains. Three selection scenarios are considered for feature behavior analysis. The domain-specific and domain-independent feature selection guidelines are summarized to facilitate future research prototyping. The implementation details of the study are discussed to standardize the task of automatic helpfulness prediction. 2. A deep neural framework is designed to enrich the interaction between review texts and star ratings during automatic helpfulness prediction. A gated convolu-tional component is introduced to learns content representations. A gated em- bedding method is proposed for encoding sophisticated yet adaptive rating infor- mation. An element alignment mechanism is proposed to explicitly capture the text-rating interaction. Ablation studies and qualitative analysis are conducted to discover insights into the interactive behavior of star ratings. 3. An end-to-end neural architecture is proposed to contextualize automatic helpful- ness prediction using review neighbors. Four weighting schemes are designed to encode a review’s surrounding neighbors as its context information into content representation learning. Three types of reviews neighbors of varied length are considered during context construction. Finally, discussions on the experimental results and the trade-o between model complexity and performance are given, along with case studies, to understand the proposed architecture.

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