@inproceedings{tonny-etal-2026-hybridscholar,
title = "{H}ybrid{S}cholar: Personalized Paper Recommendation with Cross Attention-Gated Fusion and Verified Explanation Generation",
author = "Tonny, Joytun Nessa and
Rahman, Sajidur and
Mukta, Saddam and
Shatabda, Swakkhar and
Islam, Salekul",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.13/",
pages = "275--288",
ISBN = "979-8-89176-439-2",
abstract = "The exponential growth of scientific literature has made personalized, factually grounded paper discovery increasingly difficult. Existing systems suffer from limited personalization, cold-start fragility, or hallucinated explanations. We introduce HybridScholar, an integrated architecture combining a CrossAttention-gated fusion of collaborative filtering and RAG learning candidate-specific gate weights per candidate with a Chain-of-Verification (CoVe) pipeline for factually grounded explanations. Evaluated on the Scholar Inbox dataset, HybridScholar substantially outperforms (NDCG@10=0.2991) strong baselines including LightGCN and Scholar-Inbox. Cold-start analysis shows the RAG fallback prevents hard failure for sparse users (NDCG@10=0.283), with cold NDCG rising monotonically as interactions grow. The CoVe pipeline reduces hallucination rate by 23.8{\%}, confirmed by a human evaluation study showing CoVe-revised explanations are preferred by experts on factual accuracy (76.7{\%} vs. 36.0{\%}) and by end-users on helpfulness (78.8{\%}, $p{<}0.0001$). Overall, HybridScholar improves ranking quality, cold-start robustness, and factual reliability for large-scale scholarly research discovery, demonstrated on 15,881 users across 239,618 papers."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tonny-etal-2026-hybridscholar">
<titleInfo>
<title>HybridScholar: Personalized Paper Recommendation with Cross Attention-Gated Fusion and Verified Explanation Generation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Joytun</namePart>
<namePart type="given">Nessa</namePart>
<namePart type="family">Tonny</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sajidur</namePart>
<namePart type="family">Rahman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Saddam</namePart>
<namePart type="family">Mukta</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Swakkhar</namePart>
<namePart type="family">Shatabda</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Salekul</namePart>
<namePart type="family">Islam</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yue</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ruifeng</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ayu</namePart>
<namePart type="family">Purwarianti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tong</namePart>
<namePart type="family">Rong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jun</namePart>
<namePart type="family">Suzuki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Hengqin, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-439-2</identifier>
</relatedItem>
<abstract>The exponential growth of scientific literature has made personalized, factually grounded paper discovery increasingly difficult. Existing systems suffer from limited personalization, cold-start fragility, or hallucinated explanations. We introduce HybridScholar, an integrated architecture combining a CrossAttention-gated fusion of collaborative filtering and RAG learning candidate-specific gate weights per candidate with a Chain-of-Verification (CoVe) pipeline for factually grounded explanations. Evaluated on the Scholar Inbox dataset, HybridScholar substantially outperforms (NDCG@10=0.2991) strong baselines including LightGCN and Scholar-Inbox. Cold-start analysis shows the RAG fallback prevents hard failure for sparse users (NDCG@10=0.283), with cold NDCG rising monotonically as interactions grow. The CoVe pipeline reduces hallucination rate by 23.8%, confirmed by a human evaluation study showing CoVe-revised explanations are preferred by experts on factual accuracy (76.7% vs. 36.0%) and by end-users on helpfulness (78.8%, p<0.0001). Overall, HybridScholar improves ranking quality, cold-start robustness, and factual reliability for large-scale scholarly research discovery, demonstrated on 15,881 users across 239,618 papers.</abstract>
<identifier type="citekey">tonny-etal-2026-hybridscholar</identifier>
<location>
<url>https://aclanthology.org/2026.aacl-long.13/</url>
</location>
<part>
<date>2026-11</date>
<extent unit="page">
<start>275</start>
<end>288</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T HybridScholar: Personalized Paper Recommendation with Cross Attention-Gated Fusion and Verified Explanation Generation
%A Tonny, Joytun Nessa
%A Rahman, Sajidur
%A Mukta, Saddam
%A Shatabda, Swakkhar
%A Islam, Salekul
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F tonny-etal-2026-hybridscholar
%X The exponential growth of scientific literature has made personalized, factually grounded paper discovery increasingly difficult. Existing systems suffer from limited personalization, cold-start fragility, or hallucinated explanations. We introduce HybridScholar, an integrated architecture combining a CrossAttention-gated fusion of collaborative filtering and RAG learning candidate-specific gate weights per candidate with a Chain-of-Verification (CoVe) pipeline for factually grounded explanations. Evaluated on the Scholar Inbox dataset, HybridScholar substantially outperforms (NDCG@10=0.2991) strong baselines including LightGCN and Scholar-Inbox. Cold-start analysis shows the RAG fallback prevents hard failure for sparse users (NDCG@10=0.283), with cold NDCG rising monotonically as interactions grow. The CoVe pipeline reduces hallucination rate by 23.8%, confirmed by a human evaluation study showing CoVe-revised explanations are preferred by experts on factual accuracy (76.7% vs. 36.0%) and by end-users on helpfulness (78.8%, p<0.0001). Overall, HybridScholar improves ranking quality, cold-start robustness, and factual reliability for large-scale scholarly research discovery, demonstrated on 15,881 users across 239,618 papers.
%U https://aclanthology.org/2026.aacl-long.13/
%P 275-288
Markdown (Informal)
[HybridScholar: Personalized Paper Recommendation with Cross Attention-Gated Fusion and Verified Explanation Generation](https://aclanthology.org/2026.aacl-long.13/) (Tonny et al., AACL-IJCNLP 2026)
ACL