TRec: Sequential Recommender Based On Latent Item Trend Information

TL;DR

TRec is a sequential recommendation model that incorporates item trend information from user interaction history, using self-attention and pairwise ranking to improve accuracy and efficiency in predicting user preferences.

Contribution

The paper introduces TRec, a novel approach that models item popularity trends over time and integrates this information into sequential recommendation tasks.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Maintains low runtime cost for practical deployment

Highlights the significance of item trend information in recommendations

Abstract

Recommendation system plays an important role in online web applications. Sequential recommender further models user short-term preference through exploiting information from latest user-item interaction history. Most of the sequential recommendation methods neglect the importance of ever-changing item popularity. We propose the model from the intuition that items with most user interactions may be popular in the past but could go out of fashion in recent days. To this end, this paper proposes a novel sequential recommendation approach dubbed TRec, TRec learns item trend information from implicit user interaction history and incorporates item trend information into next item recommendation tasks. Then a self-attention mechanism is used to learn better node representation. Our model is trained via pairwise rank-based optimization. We conduct extensive experiments with seven baseline methods on four benchmark datasets, The empirical result shows our approach outperforms other stateof-the-art methods while maintains a superiorly low runtime cost. Our study demonstrates the importance of item trend information in recommendation system designs, and our method also possesses great efficiency which enables it to be practical in real-world scenarios.