mlsanityData to 14 Oct 2026

Few-shot News Recommendation via Cross-lingual Transfer

arXiv 2207.14370 ↗First posted 28 Jul 2022cs.IR cs.AIcited by 3

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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.

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

20 of 41 references.

References · 20

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8 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 2.

3 papers.

  1. 2023What can Large Language Models do in chemistry? A comprehensive benchmark on eight taskscited by 185
  2. 2024Knowledge Graph Enhanced Language Agents for Recommendationcited by 11
  3. 2024News Without Borders: Domain Adaptation of Multilingual Sentence Embeddings for Cross-lingual News Recommendationcited by 4