Pre-train, Prompt, and Recommendation: A Comprehensive Survey of Language Modeling Paradigm Adaptations in Recommender Systems
The emergence of Pre-trained Language Models (PLMs) has achieved tremendous success
in the field of Natural Language Processing (NLP) by learning universal representations on …
in the field of Natural Language Processing (NLP) by learning universal representations on …
Leveraging large language models for pre-trained recommender systems
Recent advancements in recommendation systems have shifted towards more
comprehensive and personalized recommendations by utilizing large language models …
comprehensive and personalized recommendations by utilizing large language models …
Enhancing recommender systems with large language model reasoning graphs
Recommendation systems aim to provide users with relevant suggestions, but often lack
interpretability and fail to capture higher-level semantic relationships between user …
interpretability and fail to capture higher-level semantic relationships between user …
Adaptive Learning on User Segmentation: Universal to Specific Representation via Bipartite Neural Interaction
Recently, models for user representation learning have been widely applied in click-through-
rate (CTR) and conversion-rate (CVR) prediction. Usually, the model learns a universal user …
rate (CTR) and conversion-rate (CVR) prediction. Usually, the model learns a universal user …