Search Results for author:
Found 5 papers, 5 papers with code
An Empirical Study of Training ID-Agnostic Multi-modal Sequential Recommenders
Sequential Recommendation (SR) aims to predict future user-item interactions based on historical interactions.
Multi-Modality is All You Need for Transferable Recommender Systems
To align the cross-modal item representations, we propose a novel next-item enhanced cross-modal contrastive learning objective, which is equipped with both inter- and intra-modality negative samples and explicitly incorporates the transition patterns of user behaviors into the item encoders.
A Content-Driven Micro-Video Recommendation Dataset at Scale
Micro-videos have recently gained immense popularity, sparking critical research in micro-video recommendation with significant implications for the entertainment, advertising, and e-commerce industries.
NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation
The development of TransRec has encountered multiple challenges, among which the lack of large-scale, high-quality transfer learning recommendation dataset and benchmark suites is one of the biggest obstacles.
Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited
In fact, this question was answered ten years ago when IDRec beats MoRec by a strong margin in both recommendation accuracy and efficiency.
