Papers with news recommendation
Neural News Recommendation with Collaborative News Encoding and Structural User Encoding (2021.findings-emnlp)
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| Challenge: | Existing news recommendation models encode news title and content separately without leveraging the structural correlation of user browsing histories to reflect user interests explicitly. |
| Approach: | They propose a news recommendation framework consisting of collaborative news encoding and structural user encode to enhance news and user representation learning. |
| Outcome: | The proposed framework improves the performance of news recommendation on the MIND dataset. |
SentiRec: Sentiment Diversity-aware Neural News Recommendation (2020.aacl-main)
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| Challenge: | Existing news recommendation methods rank candidate news based on relevance to users’ historical browsed news, but if browsed data is dominated by certain kinds of sentiment, the model may recommend news with the same sentiment. |
| Approach: | They propose a sentiment diversity-aware neural news recommendation approach which can recommend news with more diverse sentiment without performance sacrifices. |
| Outcome: | The proposed approach can improve the sentiment diversity in news recommendation without performance sacrifice. |
NewsRecLib: A PyTorch-Lightning Library for Neural News Recommendation (2023.emnlp-demo)
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| Challenge: | Existing open source repositories expose a multitude of programming languages, libraries, and implementation differences, hindering reproducibility and extensibility. |
| Approach: | NewsRecLib is an open-source library for training and evaluating neural news recommendation models. |
| Outcome: | The open-source library provides implementations of several prominent neural models, training methods, standard evaluation benchmarks, and evaluation metrics for news recommendation. |
MINER: Multi-Interest Matching Network for News Recommendation (2022.findings-acl)
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| Challenge: | Existing methods learn a single user embedding from user’s historical behaviors to represent the reading interest. |
| Approach: | They propose a poly attention scheme to learn multiple interest vectors for each user, which encodes the different aspects of user interest. |
| Outcome: | The proposed approach significantly outperforms existing state-of-the-art methods on the MIND news recommendation benchmark. |
Neural News Recommendation with Long- and Short-term User Representations (P19-1)
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| Challenge: | Existing news recommendation methods learn a single representation for each user, which may be insufficient. |
| Approach: | They propose a neural news recommendation approach which can learn both long- and short-term user representations by using a news encoder and a user encoder. |
| Outcome: | The proposed approach can learn both long- and short-term user representations on a real-world dataset. |
TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation (2021.eacl-main)
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| Challenge: | Existing content-based recommendations fail when new users use them or a new article is created. |
| Approach: | They propose a model to transfer knowledge from a source corpus to a target corpus and use it to generate representations for unseen users in the future. |
| Outcome: | The proposed model can be used to generate representations for unseen users in the future. |
Fine-grained Interest Matching for Neural News Recommendation (2020.acl-main)
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| Challenge: | Existing studies represent each user as a single vector and then match the candidate news vector, which may lose fine-grained information for recommendation. |
| Approach: | They propose a Fine-grained interest matching method for neural news recommendation based on multi-level representations and fine-grain matching between segment pairs of each browsed news and the candidate news at each semantic level. |
| Outcome: | The proposed model can capture more fine-grained interest matching signals by performing interactions between each pair of news at multi-level semantic granularities. |
Neural News Recommendation with Topic-Aware News Representation (P19-1)
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| Challenge: | Existing methods for learning accurate news representations do not consider topic information in news. |
| Approach: | They propose a neural news recommendation approach with topic-aware news representations using CNN networks and attention networks to select important words. |
| Outcome: | The proposed approach is based on a topic-aware news encoder and user encoder. |
Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback (2022.findings-naacl)
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| Challenge: | Existing work on news recommendation only used positive and negative implicit feedback and suffered from the noise impact. |
| Approach: | They propose a denoising neural network for news recommendation with positive and negative implicit feedback, named DRPN. |
| Outcome: | The proposed method improves on the real-world large-scale dataset. |
MTRec: Multi-Task Learning over BERT for News Recommendation (2022.findings-acl)
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| Challenge: | Existing news recommendation methods learn news representations solely based on news titles. Existing methods only utilize title information and neglect other valuable news information such as categories and entities. |
| Approach: | They propose a multi-task method to incorporate multi-field information into BERT, which improves its news encoding capability. |
| Outcome: | Extensive experiments on the MIND news recommendation benchmark show the proposed method is effective. |
DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning (2022.findings-emnlp)
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| Challenge: | Existing news recommendation methods use click behaviors for interest inference and model training, but position biases can be inaccurate in targeting user interest. |
| Approach: | They propose a news recommendation method that eliminates position biases by adversarial learning by a candidate-aware click model and a bias-invariant click model. |
| Outcome: | The proposed method can effectively alleviate position biases on click behaviors and capture unbiased user interest. |
Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder (2021.emnlp-main)
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Shuqi Lu, Di He, Chenyan Xiong, Guolin Ke, Waleed Malik, Zhicheng Dou, Paul Bennett, Tie-Yan Liu, Arnold Overwijk
| Challenge: | Dense retrieval requires high-quality text sequence embeddings to support effective search in the representation space. |
| Approach: | They propose a self-learning method that pre-trains the autoencoder using a weak decoder to push the encoder to provide better sequence representations. |
| Outcome: | The proposed model significantly boosts the effectiveness and few-shot ability of dense retrieval models on web search, news recommendation, and open domain question answering. |
Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation (2022.coling-1)
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| Challenge: | Personalized news recommendation is a ubiquitous channel in various online applications, such as Google News and MSN News. |
| Approach: | They propose a plug-and-play pre-trainer to learn both user and news encoders through multi-task pre-training. |
| Outcome: | The proposed model improves on existing models and improves inference and updating time. |
Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation (2022.findings-acl)
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| Challenge: | Existing news recommender systems conduct news recall and ranking separately with different models, but maintaining multiple models leads to high computational cost and high latency. |
| Approach: | They propose a unified method for recall and ranking in news recommendation that uses historical news click behaviors to extract user embeddings for ranking from the user's attention query. |
| Outcome: | The proposed method improves recall and ranking efficiency and effectiveness on a benchmark dataset. |
NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application (2021.findings-emnlp)
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| Challenge: | Existing language models are pre-trained and distilled on general corpus like Wikipedia, which has gaps with the news domain and may be suboptimal for news intelligence. |
| Approach: | They propose a method to distill existing language models on Wikipedia to enable efficient news intelligence. |
| Outcome: | The proposed model can be used to build and test a news intelligence application on Wikipedia and Wikipedia. |
MIND: A Large-scale Dataset for News Recommendation (2020.acl-main)
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Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, Ming Zhou
| Challenge: | Personalized news recommendation is an important technique for personalized news service. |
| Approach: | They propose to build a large-scale news recommendation dataset from Microsoft News . they demonstrate that news recommendation relies on the quality of news content understanding . |
| Outcome: | The proposed dataset contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body. |
Graph Neural News Recommendation with Unsupervised Preference Disentanglement (2020.acl-main)
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| Challenge: | Existing methods to learn informative user and news representations fail to consider high-order connectivity underlying the user-news interactions. |
| Approach: | They propose a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement which can encode high-order relationships into user and news representations by information propagation along the graph. |
| Outcome: | The proposed model can encode high-order relationships into user and news representations by information propagation along the graph and disentangle latent preference factors by a neighborhood routing algorithm. |
PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity (2021.acl-long)
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| Challenge: | Existing personalized news recommendation methods have difficulties in making accurate recommendations to cold-start users. |
| Approach: | They propose to incorporate news popularity information to improve cold-start recommendations . they propose to use a popularity-aware user encoder to eliminate popularity bias . |
| Outcome: | The proposed method improves accuracy and diversity of personalized news recommendation on two real-world datasets. |
GLoCIM: Global-view Long Chain Interest Modeling for news recommendation (2025.coling-main)
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| Challenge: | Recent efforts to extract local subgraph information from click graphs have hindered collaboratively utilizing global click graph information. |
| Approach: | They propose a global-view long chain interests model that models a click graph with neighbor interest to enhance news recommendation. |
| Outcome: | The proposed method surpasses baseline methods on two real-world datasets. |
News2vec: News Network Embedding with Subnode Information (D19-1)
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| Challenge: | Existing approaches to embed news as vectors do not integrate features and inter-textual knowledge of news. |
| Approach: | They propose a model that integrates news features and inter-textual knowledge into a dense vector representation. |
| Outcome: | The proposed model can be used to represent news as a dense vector . it is compared with existing models on stock movement prediction and news recommendation tasks . |
Neural News Recommendation with Multi-Head Self-Attention (D19-1)
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| Challenge: | Precisely modeling news and users is critical for news recommendation, and capturing the contexts of words and news is important to learn news and user representations. |
| Approach: | They propose a neural news recommendation approach with multi-head self-attention to model the interactions between words and news and use multi-headed self- attention to capture relatedness between the news. |
| Outcome: | The proposed approach can learn representations from news titles by modeling the interactions between words and users and capture relatedness between the news. |
TADI: Topic-aware Attention and Powerful Dual-encoder Interaction for Recall in News Recommendation (2023.findings-emnlp)
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| Challenge: | Recent researches of news recall adopt dual-encoder architecture as it provides a much faster recall scheme and they encode each word equally. |
| Approach: | They propose a model which weights words according to news topics and a module which enhances dual-encoder interaction. |
| Outcome: | The proposed model outperforms state-of-the-art models in a series of experiments. |