Aligning Distillation For Cold-start Item Recommendation

Feiran Huang, Zefan Wang, Xiao Huang, Yufeng Qian, Zhetao Li, Hao Chen

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

24 Citations (Scopus)

Abstract

Recommending cold items in recommendation systems is a longstanding challenge due to the inherent differences between warm items, which are recommended based on user behavior, and cold items, which are recommended based on content features. To tackle this, generative models generate synthetic embeddings from content features, while dropout models enhance the robustness of the recommendation system by randomly dropping behavioral embeddings during training. However, these models primarily focus on handling the recommendation of cold items, but do not effectively address the differences between warm and cold recommendations. As a result, generative models may over-recommend either warm or cold items, neglecting the other type, and dropout models may negatively impact warm item recommendations. To address this, we propose the Aligning Distillation (ALDI) framework, which leverages warm items as "teachers" to transfer their behavioral information to cold items, referred to as "students". ALDI aligns the students with the teachers by comparing the differences in their recommendation characters, using tailored rating distribution aligning, ranking aligning, and identification aligning losses to narrow these differences. Furthermore, ALDI incorporates a teacher-qualifying weighting structure to prevent students from learning inaccurate information from unreliable teachers. Experiments on three datasets show that our approach outperforms state-of-the-art baselines in terms of overall, warm, and cold recommendation performance with three different recommendation backbones.

Original languageEnglish
Title of host publicationSIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages1147-1157
Number of pages11
ISBN (Electronic)9781450394086
DOIs
Publication statusPublished - 19 Jul 2023
Event46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023 - Taipei, Taiwan
Duration: 23 Jul 202327 Jul 2023

Publication series

NameSIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023
Country/TerritoryTaiwan
CityTaipei
Period23/07/2327/07/23

Keywords

  • aligning distillation
  • cold-start recommendation
  • content features

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Information Systems
  • Software

Fingerprint

Dive into the research topics of 'Aligning Distillation For Cold-start Item Recommendation'. Together they form a unique fingerprint.

Cite this