Mining Interest Trends and Adaptively Assigning SampleWeight for Session-based Recommendation

TL;DR

This paper introduces MTAW, a novel session-based recommendation method that models user interest trends and adaptively weights samples to improve personalization and prediction accuracy.

Contribution

The paper proposes a new approach that models instant user interests and dynamically assigns sample weights based on difficulty, enhancing SR performance.

Findings

MTAW outperforms existing SR methods on benchmark datasets.

Modeling interest trends improves recommendation relevance.

Adaptive sample weighting enhances model training efficiency.

Abstract

Session-based Recommendation (SR) aims to predict users' next click based on their behavior within a short period, which is crucial for online platforms. However, most existing SR methods somewhat ignore the fact that user preference is not necessarily strongly related to the order of interactions. Moreover, they ignore the differences in importance between different samples, which limits the model-fitting performance. To tackle these issues, we put forward the method, Mining Interest Trends and Adaptively Assigning Sample Weight, abbreviated as MTAW. Specifically, we model users' instant interest based on their present behavior and all their previous behaviors. Meanwhile, we discriminatively integrate instant interests to capture the changing trend of user interest to make more personalized recommendations. Furthermore, we devise a novel loss function that dynamically weights the samples according to their prediction difficulty in the current epoch. Extensive experimental results on two benchmark datasets demonstrate the effectiveness and superiority of our method.