Exploiting Review Neighbors for Contextualized Helpfulness Prediction

TL;DR

This paper introduces a neural architecture that leverages sequential neighbors of online reviews to improve helpfulness prediction, highlighting the importance of context in user perception.

Contribution

It proposes the first end-to-end neighbor-aware helpfulness prediction model that incorporates surrounding review context using multiple weighting schemes.

Findings

Neighbor-aware prediction outperforms state-of-the-art baselines.

Closeness of neighbors significantly impacts prediction accuracy.

Considering up to five closest neighbors yields effective results.

Abstract

Helpfulness prediction techniques have been widely used to identify and recommend high-quality online reviews to customers. Currently, the vast majority of studies assume that a review's helpfulness is self-contained. In practice, however, customers hardly process reviews independently given the sequential nature. The perceived helpfulness of a review is likely to be affected by its sequential neighbors (i.e., context), which has been largely ignored. This paper proposes a new methodology to capture the missing interaction between reviews and their neighbors. The first end-to-end neural architecture is developed for neighbor-aware helpfulness prediction (NAP). For each review, NAP allows for three types of neighbor selection: its preceding, following, and surrounding neighbors. Four weighting schemes are designed to learn context clues from the selected neighbors. A review is then contextualized into the learned clues for neighbor-aware helpfulness prediction. NAP is evaluated on six domains of real-world online reviews against a series of state-of-the-art baselines. Extensive experiments confirm the effectiveness of NAP and the influence of sequential neighbors on a current reviews. Further hyperparameter analysis reveals three main findings. (1) On average, eight neighbors treated with uneven importance are engaged for context construction. (2) The benefit of neighbor-aware prediction mainly results from closer neighbors. (3) Equally considering up to five closest neighbors of a review can usually produce a weaker but tolerable prediction result.