# Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation

arXiv: https://arxiv.org/abs/2205.06058 · HTML: https://mlsanity.com/paper/2205.06058/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 61 references that are in the map.
- First posted on arXiv 12 May 2022.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [A Theoretical Analysis of Contrastive Unsupervised Representation Learning](https://mlsanity.com/paper/1902.09229/) (2019)
- [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)
- [RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/1803.03467/) (2018)
- [DKN: Deep Knowledge-Aware Network for News Recommendation](https://mlsanity.com/paper/1801.08284/) (2018)
- [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019)
- [Hierarchical Gating Networks for Sequential Recommendation](https://mlsanity.com/paper/1906.09217/) (2019)
- [NPA: Neural News Recommendation with Personalized Attention](https://mlsanity.com/paper/1907.05559/) (2019)
- [Graph Enhanced Representation Learning for News Recommendation](https://mlsanity.com/paper/2003.14292/) (2020)
- [D'ej`a vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation](https://mlsanity.com/paper/2002.00741/) (2020)
- [News Session-Based Recommendations using Deep Neural Networks](https://mlsanity.com/paper/1808.00076/) (2018)
- [Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks](https://mlsanity.com/paper/1904.10367/) (2019)

## Built on by (most cited first, 9 of 9)

- [Prompt Learning for News Recommendation](https://mlsanity.com/paper/2304.05263/) (2023)
- [HoME: Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou](https://mlsanity.com/paper/2408.05430/) (2024)
- [CROWN: A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendation](https://mlsanity.com/paper/2310.09401/) (2023)
- [FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation](https://mlsanity.com/paper/2304.04529/) (2023)
- [Impression-Aware Recommender Systems](https://mlsanity.com/paper/2308.07857/) (2023)
- [Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution](https://mlsanity.com/paper/2310.03032/) (2023)
- [Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation](https://mlsanity.com/paper/2409.17711/) (2024)
- [Enhancing AI Assisted Writing with One-Shot Implicit Negative Feedback](https://mlsanity.com/paper/2410.11009/) (2024)
- [Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation](https://mlsanity.com/paper/2508.13064/) (2025)

## Abstract (arXiv)

News recommendation for anonymous readers is a useful but challenging task for many news portals, where interactions between readers and articles are limited within a temporary login session. Previous works tend to formulate session-based recommendation as a next item prediction task, while they neglect the implicit feedback from user behaviors, which indicates what users really like or dislike. Hence, we propose a comprehensive framework to model user behaviors through positive feedback (i.e., the articles they spend more time on) and negative feedback (i.e., the articles they choose to skip without clicking in). Moreover, the framework implicitly models the user using their session start time, and the article using its initial publishing time, in what we call "neutral feedback". Empirical evaluation on three real-world news datasets shows the framework's promising performance of more accurate, diverse and even unexpectedness recommendations than other state-of-the-art session-based recommendation approaches.