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        <title>SAND: Subjective-Anticipation-Augmented News Recommendation via Dual-Pathway Distillation</title>
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        <namePart type="given">Hong-Kyun</namePart>
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    <abstract>On news platforms, a user clicks on an article based on her subjective anticipation of its content, inferred from the title using her background knowledge. We introduce SAND (Subjective Anticipation-augmented News Recommendation via Dual-pathway Distillation), the first framework to explicitly incorporate this user-specific anticipation that drives click behavior, formalized as the anticipated body, into news modeling. SAND (i) generates personalized anticipated bodies via an agent-based collaborative system, and (ii) learns preferences from anticipated bodies using a candidate-aware attention network with dual-pathway knowledge distillation. Experiments on real-world news datasets (i.e., MIND and Adressa) demonstrate that SAND significantly outperforms eleven state-of-the-art baselines.</abstract>
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%0 Conference Proceedings
%T SAND: Subjective-Anticipation-Augmented News Recommendation via Dual-Pathway Distillation
%A Bae, Hong-Kyun
%A Jin, Zhenping
%A Kim, Sang-Wook
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F bae-etal-2026-sand
%X On news platforms, a user clicks on an article based on her subjective anticipation of its content, inferred from the title using her background knowledge. We introduce SAND (Subjective Anticipation-augmented News Recommendation via Dual-pathway Distillation), the first framework to explicitly incorporate this user-specific anticipation that drives click behavior, formalized as the anticipated body, into news modeling. SAND (i) generates personalized anticipated bodies via an agent-based collaborative system, and (ii) learns preferences from anticipated bodies using a candidate-aware attention network with dual-pathway knowledge distillation. Experiments on real-world news datasets (i.e., MIND and Adressa) demonstrate that SAND significantly outperforms eleven state-of-the-art baselines.
%U https://aclanthology.org/2026.emnlp-main.1624/
%P 36934-36951

Markdown (Informal)

[SAND: Subjective-Anticipation-Augmented News Recommendation via Dual-Pathway Distillation](https://aclanthology.org/2026.emnlp-main.1624/) (Bae et al., EMNLP 2026)

• SAND: Subjective-Anticipation-Augmented News Recommendation via Dual-Pathway Distillation (Bae et al., EMNLP 2026)

ACL

• Hong-Kyun Bae, Zhenping Jin, and Sang-Wook Kim. 2026. SAND: Subjective-Anticipation-Augmented News Recommendation via Dual-Pathway Distillation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 36934–36951, Budapest, Hungary. Association for Computational Linguistics.