Embedding-based News Recommendation for Millions of Users

By Shumpei Okura, Yukihiro Tagami, Shingo Ono, Akira Tajima. 2017

502 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xing Xie
3. Xiang Wang
4. Fangzhao Wu
5. Chuhan Wu
6. Meng Wang
7. Yongfeng Huang
8. Tat-seng Chua

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Xing Xie
4. Fangzhao Wu
5. Chuhan Wu
6. Meng Wang
7. Yongfeng Huang
8. Le Wu

Citations in the corpus

• DKN: Deep Knowledge-Aware Network for News Recommendation (2018, background): “To alleviate the impact of information over-loading, it is critical to help users target their reading interests and make personalized recommendations [2, 25, 27, 32, 34, 39].”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “A denoising autoencoder with weak supervision is proposed to learn the distributed representation vector of each item [80].”
• When large language models meet personalization: perspectives of challenges and opportunities (2023, unknown): “[94]evaluatediffer-ent network architectures, including word-models and GRU networks, for representing user states.”
• Real-time Personalization using Embeddings for Search Ranking at Airbnb (2018, background): “title and description) to compute their embeddings [5, 14, 19, 28].”
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, methodology): “Therefore, methods of natural language processing can be combined with recommendation models for extracting news features better [134].”
• Neural News Recommendation with Long- and Short-term User Representations (2019, methodology): “In recent years, several deep learning meth-ods were proposed for personalized news recommendation (Wang et al., 2018; Okura et al., 2017; Zheng et al., 2018).”
• MIND: A Large-scale Dataset for News Recommendation (2020, methodology): “, how to represent news articles which have rich textual content and how to model users’ interest in news from their previous behaviors (Okura et al., 2017).”
• Neural News Recommendation with Multi-Head Self-Attention (2019, methodology): “Following (Okura et al., 2017), the click probability score ŷ is computed by the inner product of the user representation vector and the news representation vector, i.”
• Cross-domain Recommendation Without Sharing User-relevant Data (2019, unknown): “Besides, some works utilized neural networks to extract the auxiliary information and features in recommender systems, such as textual [25, 47], visual [3, 38], video [5], and hybrid [48].”
• NewsEmbed: Modeling News through Pre-trained Document Representations (2021, methodology): “[44], but with a much higher granularity and scale of data.”
• NPA: Neural News Recommendation with Personalized Attention (2019, methodology): “Okura et al. [24] proposed to learn news embeddings based on the similarities between news articles in the same and different categories.”
• Deep Learning Based Recommender System (2017, methodology): “Recently, many companies employ deep learning for further enhancing their recommendation quality [20, 27, 113].”
• FedRec: Privacy-Preserving News Recommendation with Federated Learning (2020, methodology): “For example, Okura et al. (2017) proposed to learn news representations from the content of news articles via autoencoder, and learn user interest representations from the clicked news articles via Gated Recurrent Unit (GRU) network.”
• Recommendation system based on deep learning methods: a systematic review and new directions (2019, background): “In recent times, different companies have exploited deep learning methods for further improving the diversity and performance of their RSs (Cheng et al. 2016; Okura et al. 2017).”