# PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity

arXiv: https://arxiv.org/abs/2106.01300 · HTML: https://mlsanity.com/paper/2106.01300/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2021.

## Built on by (most cited first, 14 of 14)

- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [Prompt Learning for News Recommendation](https://mlsanity.com/paper/2304.05263/) (2023)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [DIGAT: Modeling News Recommendation with Dual-Graph Interaction](https://mlsanity.com/paper/2210.05196/) (2022)
- [CROWN: A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendation](https://mlsanity.com/paper/2310.09401/) (2023)
- [A Survey on LLM-based News Recommender Systems](https://mlsanity.com/paper/2502.09797/) (2025)
- [On the Overlooked Significance of Underutilized Contextual Features in Recent News Recommendation Models](https://mlsanity.com/paper/2112.14370/) (2021)
- [Quality-aware News Recommendation](https://mlsanity.com/paper/2202.13605/) (2022)
- [Doctor Recommendation in Online Health Forums via Expertise Learning](https://mlsanity.com/paper/2203.02932/) (2022)
- [Peeling Back the Layers: An In-Depth Evaluation of Encoder Architectures in Neural News Recommenders](https://mlsanity.com/paper/2410.01470/) (2024)
- [FeatureBox: Feature Engineering on GPUs for Massive-Scale Ads Systems](https://mlsanity.com/paper/2210.07768/) (2022)
- [Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up](https://mlsanity.com/paper/2302.14395/) (2023)
- [A Look Into News Avoidance Through AWRS: An Avoidance-Aware Recommender System](https://mlsanity.com/paper/2407.09137/) (2024)
- [Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation](https://mlsanity.com/paper/2508.13064/) (2025)

## Abstract (arXiv)

Personalized news recommendation methods are widely used in online news services. These methods usually recommend news based on the matching between news content and user interest inferred from historical behaviors. However, these methods usually have difficulties in making accurate recommendations to cold-start users, and tend to recommend similar news with those users have read. In general, popular news usually contain important information and can attract users with different interests. Besides, they are usually diverse in content and topic. Thus, in this paper we propose to incorporate news popularity information to alleviate the cold-start and diversity problems for personalized news recommendation. In our method, the ranking score for recommending a candidate news to a target user is the combination of a personalized matching score and a news popularity score. The former is used to capture the personalized user interest in news. The latter is used to measure time-aware popularity of candidate news, which is predicted based on news content, recency, and real-time CTR using a unified framework. Besides, we propose a popularity-aware user encoder to eliminate the popularity bias in user behaviors for accurate interest modeling. Experiments on two real-world datasets show our method can effectively improve the accuracy and diversity for news recommendation.