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Found 5 papers, 3 papers with code
Sequence-level Semantic Representation Fusion for Recommender Systems
The core idea of our approach is to conduct a sequence-level semantic fusion approach by better integrating global contexts.
Prompting Large Language Models for Recommender Systems: A Comprehensive Framework and Empirical Analysis
As for the use of LLMs as recommenders, we analyze the impact of public availability, tuning strategies, model architecture, parameter scale, and context length on recommendation results based on the classification of LLMs.
Recent Advances in RecBole: Extensions with more Practical Considerations
In order to show the recent update in RecBole, we write this technical report to introduce our latest improvements on RecBole.
RecBole 2.0: Towards a More Up-to-Date Recommendation Library
In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures.
Negative Sampling for Contrastive Representation Learning: A Review
The learn-to-compare paradigm of contrastive representation learning (CRL), which compares positive samples with negative ones for representation learning, has achieved great success in a wide range of domains, including natural language processing, computer vision, information retrieval and graph learning.
