Train Once, Use Flexibly: A Modular Framework for Multi-Aspect Neural News Recommendation

Andreea Iana, Goran Glavaš, Heiko Paulheim


Abstract
Recent neural news recommenders (NNRs) extend content-based recommendation (1) by aligning additional aspects (e.g., topic, sentiment) between candidate news and user history or (2) by diversifying recommendations w.r.t. these aspects. This customization is achieved by ”hardcoding” additional constraints into the NNR’s architecture and/or training objectives: any change in the desired recommendation behavior thus requires retraining the model with a modified objective. This impedes widespread adoption of multi-aspect news recommenders. In this work, we introduce MANNeR, a modular framework for multi-aspect neural news recommendation that supports on-the-fly customization over individual aspects at inference time. With metric-based learning as its backbone, MANNeR learns aspect-specialized news encoders and then flexibly and linearly combines the resulting aspect-specific similarity scores into different ranking functions, alleviating the need for ranking function-specific retraining of the model. Extensive experimental results show that MANNeR consistently outperforms state-of-the-art NNRs on both standard content-based recommendation and single- and multi-aspect customization. Lastly, we validate that MANNeR’s aspect-customization module is robust to language and domain transfer.
Anthology ID:
2024.findings-emnlp.558
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2024
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9555–9571
Language:
URL:
https://aclanthology.org/2024.findings-emnlp.558
DOI:
10.18653/v1/2024.findings-emnlp.558
Bibkey:
Cite (ACL):
Andreea Iana, Goran Glavaš, and Heiko Paulheim. 2024. Train Once, Use Flexibly: A Modular Framework for Multi-Aspect Neural News Recommendation. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 9555–9571, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Train Once, Use Flexibly: A Modular Framework for Multi-Aspect Neural News Recommendation (Iana et al., Findings 2024)
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PDF:
https://aclanthology.org/2024.findings-emnlp.558.pdf