# A Simple Yet Effective Approach for Diversified Session-Based Recommendation

arXiv: https://arxiv.org/abs/2404.00261 · HTML: https://mlsanity.com/paper/2404.00261/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 42 references that are in the map.
- First posted on arXiv 30 Mar 2024.

## Builds on (most cited first, 21 of 21)

- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [A Survey on Session-based Recommender Systems](https://mlsanity.com/paper/1902.04864/) (2019)
- [Controllable Multi-Interest Framework for Recommendation](https://mlsanity.com/paper/2005.09347/) (2020)
- [Improved Recurrent Neural Networks for Session-based Recommendations](https://mlsanity.com/paper/1606.08117/) (2016)
- [Global Context Enhanced Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2106.05081/) (2021)
- [RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation](https://mlsanity.com/paper/1812.02646/) (2018)
- [Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1911.11942/) (2019)
- [A Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2207.12515/) (2022)
- [Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation](https://mlsanity.com/paper/2103.03578/) (2021)
- [Evaluation of Session-based Recommendation Algorithms](https://mlsanity.com/paper/1803.09587/) (2018)
- [TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2005.02844/) (2020)
- [DGCN: Diversified Recommendation with Graph Convolutional Networks](https://mlsanity.com/paper/2108.06952/) (2021)
- [Research Commentary on Recommendations with Side Information: A Survey and Research Directions](https://mlsanity.com/paper/1909.12807/) (2019)
- [Trustworthy Recommender Systems](https://mlsanity.com/paper/2208.06265/) (2022)
- [Improving End-to-End Sequential Recommendations with Intent-aware Diversification](https://mlsanity.com/paper/1908.10171/) (2019)
- [Recommending Accurate and Diverse Items Using Bilateral Branch Network](https://mlsanity.com/paper/2101.00781/) (2021)
- [Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System](https://mlsanity.com/paper/2008.13526/) (2020)
- [Understanding Diversity in Session-Based Recommendation](https://mlsanity.com/paper/2208.13453/) (2022)

## Abstract (arXiv)

Session-based recommender systems (SBRSs) have become extremely popular in view of the core capability of capturing short-term and dynamic user preferences. However, most SBRSs primarily maximize recommendation accuracy but ignore user minor preferences, thus leading to filter bubbles in the long run. Only a handful of works, being devoted to improving diversity, depend on unique model designs and calibrated loss functions, which cannot be easily adapted to existing accuracy-oriented SBRSs. It is thus worthwhile to come up with a simple yet effective design that can be used as a plugin to facilitate existing SBRSs on generating a more diversified list in the meantime preserving the recommendation accuracy. In this case, we propose an end-to-end framework applied for every existing representative (accuracy-oriented) SBRS, called diversified category-aware attentive SBRS (DCA-SBRS), to boost the performance on recommendation diversity. It consists of two novel designs: a model-agnostic diversity-oriented loss function, and a non-invasive category-aware attention mechanism. Extensive experiments on three datasets showcase that our framework helps existing SBRSs achieve extraordinary performance in terms of recommendation diversity and comprehensive performance, without significantly deteriorating recommendation accuracy compared to state-of-the-art accuracy-oriented SBRSs.