Neighbor-aware review helpfulness prediction

Highlights：

• In practice, review helpfulness is hardly perceived independently since reviews are displayed in sequence.

• We interact a review with its adjacent counterparts (i.e., neighbors) in the sequence during helpfulness prediction.

• We design 12 (three selection × four aggregation) schemes to learn the context clues of a review from its neighbors.

• Extensive experiments on six domains of real-world reviews show that our model reaches the new state-of-the-art.

• We conduct further analysis on our model to investigate how reviews are influenced by their neighbors.

摘要

Helpfulness prediction techniques have been widely incorporated into online decision support systems to identify high-quality reviews. Most current studies on helpfulness prediction assume that a review's helpfulness only relies on information from itself. In practice, however, consumers hardly process reviews independently because reviews are displayed in sequence; a review is more likely to be affected by its adjacent neighbors in the sequence, which is largely understudied. In this paper, we proposed the first end-to-end neural architecture to capture the missing interaction between reviews and their neighbors. Our model allows for a total of 12 (three selection × four aggregation) schemes that contextualize a review into the context clues learned from its neighbors. We evaluated our model on six domains of real-world online reviews against a series of state-of-the-art baselines. Experimental results confirm the influence of sequential neighbors on reviews and show that our model significantly outperforms the baselines by 1% to 5%. We further revealed how reviews are influenced by their neighbors during helpfulness perception via extensive analysis. The results and findings of our work provide theoretical contributions to the field of review helpfulness prediction and offer insights into practical decision support system design.