# Few-shot News Recommendation via Cross-lingual Transfer

arXiv: https://arxiv.org/abs/2207.14370 · HTML: https://mlsanity.com/paper/2207.14370/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019)
- [CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP](https://mlsanity.com/paper/2006.06402/) (2020)
- [NPA: Neural News Recommendation with Personalized Attention](https://mlsanity.com/paper/1907.05559/) (2019)
- [Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning](https://mlsanity.com/paper/2106.06937/) (2021)
- [Empowering News Recommendation with Pre-trained Language Models](https://mlsanity.com/paper/2104.07413/) (2021)
- [Neural News Recommendation with Attentive Multi-View Learning](https://mlsanity.com/paper/1907.05576/) (2019)
- [BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages](https://mlsanity.com/paper/1710.02187/) (2017)
- [Zero-Shot Recommender Systems](https://mlsanity.com/paper/2105.08318/) (2021)
- [Personalized News Recommendation: Methods and Challenges](https://mlsanity.com/paper/2106.08934/) (2021)
- [From Zero-Shot Learning to Cold-Start Recommendation](https://mlsanity.com/paper/1906.08511/) (2019)
- [Unsupervised Cross-lingual Adaptation for Sequence Tagging and Beyond](https://mlsanity.com/paper/2010.12405/) (2020)
- [NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application](https://mlsanity.com/paper/2102.04887/) (2021)
- [News Session-Based Recommendations using Deep Neural Networks](https://mlsanity.com/paper/1808.00076/) (2018)
- [Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation](https://mlsanity.com/paper/2007.07085/) (2020)
- [TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation](https://mlsanity.com/paper/2101.05611/) (2021)

## Built on by (most cited first, 3 of 3)

- [What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks](https://mlsanity.com/paper/2305.18365/) (2023)
- [Knowledge Graph Enhanced Language Agents for Recommendation](https://mlsanity.com/paper/2410.19627/) (2024)
- [News Without Borders: Domain Adaptation of Multilingual Sentence Embeddings for Cross-lingual News Recommendation](https://mlsanity.com/paper/2406.12634/) (2024)

## Abstract (arXiv)

The cold-start problem has been commonly recognized in recommendation systems and studied by following a general idea to leverage the abundant interaction records of warm users to infer the preference of cold users. However, the performance of these solutions is limited by the amount of records available from warm users to use. Thus, building a recommendation system based on few interaction records from a few users still remains a challenging problem for unpopular or early-stage recommendation platforms. This paper focuses on solving the few-shot recommendation problem for news recommendation based on two observations. First, news at different platforms (even in different languages) may share similar topics. Second, the user preference over these topics is transferable across different platforms. Therefore, we propose to solve the few-shot news recommendation problem by transferring the user-news preference from a many-shot source domain to a few-shot target domain. To bridge two domains that are even in different languages and without any overlapping users and news, we propose a novel unsupervised cross-lingual transfer model as the news encoder that aligns semantically similar news in two domains. A user encoder is constructed on top of the aligned news encoding and transfers the user preference from the source to target domain. Experimental results on two real-world news recommendation datasets show the superior performance of our proposed method on addressing few-shot news recommendation, comparing to the baselines.