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pith:UO7YVABA

pith:2026:UO7YVABAGTPH7QMDREGJAU4C4G

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Generative Long-term User Interest Modeling for Click-Through Rate Prediction

Bo Zhang, Hao Fang, Huimu Ye, Jiangli Shao, Kaifu Zheng, Shu Han, Xingxing Wang, Zhiwei Liu

A generative module produces multiple target-independent interest distributions to capture diverse long-term user behaviors for click-through rate prediction.

arxiv:2605.15905 v1 · 2026-05-15 · cs.IR · cs.AI

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Claims

C1strongest claim

Based on the generation process, GenLI improves the diversity of user interests and avoids complex matching-based behavioral retrieval, achieving a better balance between accuracy and efficiency for CTR prediction.

C2weakest assumption

The interest generation module produces distributions that faithfully represent multiple latent aspects of real user interests even when generated without any information about the target item, and the simple lookup operation in the behavior retrieval module selects behaviors that remain sufficiently relevant for downstream interest fusion.

C3one line summary

GenLI generates diverse target-independent interest distributions via an IGM, retrieves behaviors with O(1) lookup in BRM, and fuses via IFM gating to balance accuracy and efficiency in CTR prediction.

References

43 extracted · 43 resolved · 3 Pith anchors

[1] Yue Cao, Xiaojiang Zhou, Jiaqi Feng, Peihao Huang, Yao Xiao, Dayao Chen, and Sheng Chen. 2022. Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction. InProceedings of the 31 2022

[2] Jianxin Chang, Chenbin Zhang, Zhiyi Fu, Xiaoxue Zang, Lin Guan, Jing Lu, Yiqun Hui, Dewei Leng, Yanan Niu, Yang Song, and Kun Gai. 2023. TWIN: TWo-stage Interest Network for Lifelong User Behavior Mod 2023

[3] Junxuan Chen, Baigui Sun, Hao Li, Hongtao Lu, and Xian-Sheng Hua. 2016. Deep CTR Prediction in Display Advertising. InProceedings of the 24th ACM International Conference on Multimedia(Amsterdam, The 2016

[4] Qiwei Chen, Changhua Pei, Shanshan Lv, Chao Li, Junfeng Ge, and Wenwu Ou

[5] End-to-end user behavior retrieval in click-through rateprediction model 2021

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Receipt and verification

First computed	2026-05-20T00:01:24.791252Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a3bf8a802034de7fc183890c905382e181484e42937c725251860ec30397b01a

Aliases

arxiv: 2605.15905 · arxiv_version: 2605.15905v1 · doi: 10.48550/arxiv.2605.15905 · pith_short_12: UO7YVABAGTPH · pith_short_16: UO7YVABAGTPH7QMD · pith_short_8: UO7YVABA

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/UO7YVABAGTPH7QMDREGJAU4C4G \
  | jq -c '.canonical_record' \
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# expect: a3bf8a802034de7fc183890c905382e181484e42937c725251860ec30397b01a

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
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    "submitted_at": "2026-05-15T12:42:54Z",
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