Yinwei Wei

Contrastive Learning for Cold-Start Recommendation

2021 · arXiv (Cornell University)

Recommending cold-start items is a long-standing and fundamental challenge in recommender systems. Without any historical interaction on cold-start items, CF scheme fails to use collaborative signals to infer user preference on these items. To solve …

General Debiasing for Multimodal Sentiment Analysis

2023

Existing work on Multimodal Sentiment Analysis (MSA) utilizes multimodal information for prediction yet unavoidably suffers from fitting the spurious correlations between multimodal features and sentiment labels. For example, if most videos with a blue background …

MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation

2023 · arXiv (Cornell University)

Bundle recommendation seeks to recommend a bundle of related items to users to improve both user experience and the profits of platform. Existing bundle recommendation models have progressed from capturing only user-bundle interactions to the …

Data-efficient Fine-tuning for LLM-based Recommendation

2024 · arXiv (Cornell University)

Leveraging Large Language Models (LLMs) for recommendation has recently garnered considerable attention, where fine-tuning plays a key role in LLMs' adaptation. However, the cost of fine-tuning LLMs on rapidly expanding recommendation data limits their practical …

MMGCN

2019

Personalized recommendation plays a central role in many online content sharing platforms. To provide quality micro-video recommendation service, it is of crucial importance to consider the interactions between users and items (i.e. micro-videos) as well …