Benefiting from Negative yet Informative Feedback by Contrasting Opposing Sequential Patterns

TL;DR

This paper introduces a novel sequential recommendation model that leverages both positive and negative user feedback through transformer encoders and a contrastive loss, improving recommendation accuracy and reducing negative item promotion.

Contribution

It proposes a dual-transformer framework with a contrastive loss to effectively utilize negative feedback in sequential recommendation systems, which is a novel approach.

Findings

Improves true-positive recommendation metrics.

Reduces wrongly promoted negative items.

Outperforms state-of-the-art methods.

Abstract

We consider the task of learning from both positive and negative feedback in a sequential recommendation scenario, as both types of feedback are often present in user interactions. Meanwhile, conventional sequential learning models usually focus on considering and predicting positive interactions, ignoring that reducing items with negative feedback in recommendations improves user satisfaction with the service. Moreover, the negative feedback can potentially provide a useful signal for more accurate identification of true user interests. In this work, we propose to train two transformer encoders on separate positive and negative interaction sequences. We incorporate both types of feedback into the training objective of the sequential recommender using a composite loss function that includes positive and negative cross-entropy as well as a cleverly crafted contrastive term, that helps better modeling opposing patterns. We demonstrate the effectiveness of this approach in terms of increasing true-positive metrics compared to state-of-the-art sequential recommendation methods while reducing the number of wrongly promoted negative items.