107 citations · 244 across the 24 of their papers we have counts for
14 papers · 1 filter
LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation
Yingzhi He, Xiaohao Liu, An Zhang +2
Sequential recommendation aims to predict users' future interactions by modeling collaborative filtering (CF) signals from historical behaviors of similar users or items. Tradition…
Integrate Temporal Graph Learning into LLM-based Temporal Knowledge Graph Model
He Chang, Jie Wu, Zhulin Tao +3
Temporal Knowledge Graph Forecasting (TKGF) aims to predict future events based on the observed events in history. Recently, Large Language Models (LLMs) have exhibited remarkable…
Fine-tuning Multimodal Large Language Models for Product Bundling
Xiaohao Liu, Jie Wu, Zhulin Tao +3
Recent advances in product bundling have leveraged multimodal information through sophisticated encoders, but remain constrained by limited semantic understanding and a narrow scop…
LARP: Language Audio Relational Pre-training for Cold-Start Playlist Continuation
Rebecca Salganik, Xiaohao Liu, Yunshan Ma +2
As online music consumption increasingly shifts towards playlist-based listening, the task of playlist continuation, in which an algorithm suggests songs to extend a playlist in a…
CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling
Yunshan Ma, Yingzhi He, Wenjun Zhong +3
Product bundling has been a prevailing marketing strategy that is beneficial in the online shopping scenario. Effective product bundling methods depend on high-quality item represe…
Contrastive Pre-training for Deep Session Data Understanding
Zixuan Li, Lizi Liao, Yunshan Ma +1
Session data has been widely used for understanding user's behavior in e-commerce. Researchers are trying to leverage session data for different tasks, such as purchase intention p…
SCTc-TE: A Comprehensive Formulation and Benchmark for Temporal Event Forecasting
Yunshan Ma, Chenchen Ye, Zijian Wu +4
Temporal complex event forecasting aims to predict the future events given the observed events from history. Most formulations of temporal complex event are unstructured or without…
Enhancing Item-level Bundle Representation for Bundle Recommendation
Xiaoyu Du, Kun Qian, Yunshan Ma +1
Bundle recommendation approaches offer users a set of related items on a particular topic. The current state-of-the-art (SOTA) method utilizes contrastive learning to learn represe…
MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation
Yunshan Ma, Yingzhi He, Xiang Wang +4
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…
Leveraging Multimodal Features and Item-level User Feedback for Bundle Construction
Yunshan Ma, Xiaohao Liu, Yinwei Wei +3
Automatic bundle construction is a crucial prerequisite step in various bundle-aware online services. Previous approaches are mostly designed to model the bundling strategy of exis…
CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation
Yunshan Ma, Yingzhi He, An Zhang +2
Bundle recommendation aims to recommend a bundle of related items to users, which can satisfy the users' various needs with one-stop convenience. Recent methods usually take advant…
Knowledge Enhanced Neural Fashion Trend Forecasting
Yunshan Ma, Yujuan Ding, Xun Yang +3
Fashion trend forecasting is a crucial task for both academia and industry. Although some efforts have been devoted to tackling this challenging task, they only studied limited fas…
Automatic Fashion Knowledge Extraction from Social Media
Yunshan Ma, Lizi Liao, Tat-Seng Chua
Fashion knowledge plays a pivotal role in helping people in their dressing. In this paper, we present a novel system to automatically harvest fashion knowledge from social media. I…
TransNFCM: Translation-Based Neural Fashion Compatibility Modeling
Xun Yang, Yunshan Ma, Lizi Liao +2
Identifying mix-and-match relationships between fashion items is an urgent task in a fashion e-commerce recommender system. It will significantly enhance user experience and satisf…