On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction (2023)

Abstract: Helpful reviews have been essential for the success of e-commerce services, as they help customers make quick purchase decisions and benefit the merchants in their sales. While many reviews are informative, others provide little value and may contain spam, excessive appraisal, or unexpected biases. With the large volume of reviews and their uneven quality, the problem of detecting helpful reviews has drawn much attention lately. Existing methods for identifying helpful reviews primarily focus on review text and ignore the two key factors of (1) who post the reviews and (2) when the reviews are posted. Moreover, the helpfulness votes suffer from scarcity for less popular products and recently submitted (a.k.a., cold-start) reviews. To address these challenges, we introduce a dataset and develop a model that integrates the reviewer’s expertise, derived from the past review history of the reviewers, and the temporal dynamics of the reviews to automatically assess review helpfulness. We conduct experiments on our dataset to demonstrate the effectiveness of incorporating these factors and report improved results compared to several well-established baselines.

## introduction many customers rely on online reviews from nonprofessionals, on daily basis, to decide what products to buy (e.g., amazon), what hotels to stay at (e.g., tripadvisor), what restaurants to eat (e.g., yelp) and even what books to read (e.g., goodreads). a recent survey of bizrate insights reward members found that approximately 98% of online shoppers research a vendor via online reviews before making a purchase decision @xcite . since the reviews are expected to describe the actual experiences and opinions of users, they can provide a reliable source of reference, improving other customers' confidence, comfort, and the overall shopping experience @xcite et @xcite . however, despite their tremendous benefits, online reviews are often of mixed qualities. while many reviews are informative, others provide little value and may contain excessive appraisal or spam (see figure 1-c ). there are multiple factors that affect the quality of a review, including the reviewers' life experience, educational background, and the motive for writing the review @xcite , and these factors are not usually explicit in the review text. all these pose challenges for customers who are less experienced in a subject area and need the reviews the most, simply because there is less incentive for more experienced users to use the reviews. moreover, customers usually have limited patience for reading reviews -most customers read less than 10 reviews before mak-ing a purchase decision about an item @xcite . the large volume of reviews and their unpredictable quality and the limited customer patience demand better review utilization strategies to manage the information overload. one standard method to identify more informative reviews is to ask for feedback from customers or site visitors who read them. by asking, "was this review helpful to you?," or "did you find this review helpful?" at the end of each review, online platforms can crowdsource helpfulness votes from other customers. as a result, user reviews that gain the most helpful votes are shown first to the potential buyers to make the decision easier. however, the voting data suffers from scarcity @xcite since only a tiny proportion of customers are willing to cast helpfulness votes. the scarcity is even more severe in reviews of less popular products and more recently submitted reviews (a.k.a., cold-start reviews) @xcite , despite the fact that more recent reviews may in fact contain more relevant and time-sensitive information (e.g., "new covid restrictions" or "dirty pool area") as shown in figure 1 -b but no helpfulness vote. in this paper, we study the confluence of the reviewing history of reviewers and the review text for helpfulness identification. first, we observe that people who post more reviews and earn more helpful votes are more likely to be better reviewers. second, trustworthy reviewers (e.g., figure 1-a ) are less likely to be posting fake or biased reviews, and their reviews are more likely to earn more helpful votes; otherwise, they will be ruining their reputation. third, those who have been to more hotels or restaurants across different cities have a better basis for comparison and writing critical reviews. to the best of our knowledge, existing works only focus on review content and neglect the reviewers and their reviewing history. integrating the review text with the reviewing history of the reviewers is the problem studied in this paper. our main contributions are summarized as follows: ## related work more traditional approaches on review helpfulness prediction focus solely on the text of reviews, and some consider both text and images to guide the prediction. in general, the task can be addressed using a predictive model based on hand-crafted features such as structural (susan and david, 2010; xiong and litman, 2014), lexical @xcite , syntactic @xcite , emotional @xcite , semantic @xcite , and arguments @xcite from the review text. these features may be fed into a conventional classifier such as svm, random forest, or gradient boosting to identify helpful reviews. these methods heavily rely on manual feature engineering, which is laborintensive and time-consuming. inspired by the remarkable progress of deep neural networks, more recent studies make use of deep neural models, which can learn both intrinsic and extrinsic features given labeled data. @xcite uses a text-based cnn model to automatically capture the character-level, word-level, and topic-level features for helpfulness prediction. @xcite uses an end-to-end multi-task neural architecture with the help of an auxiliary task, such as rating regression, to boost the performance of the review helpfulness identification. @xcite and @xcite use both text and images to guide the review helpfulness prediction. since the image field is usually optional in reviews, a large volume of reviews contain only text, for which these multimodal models would produce inconsistent results. ## conclusion and future work in this paper, we develop a model incorporating the reviewer's expertise and temporal information in reviews to predict the helpfulness, especially for unreliable and cold-start reviews. furthermore, we present a detailed analysis to interpret the model behavior and provide reasoning behind model predictions. for future work, we will look into the problem of personalized review helpfulness prediction to model the demographics and cultural differences of the reviewers.