Self-supervised learning for recommender systems: A survey
In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …
tremendous success, but they still fall short of expectation when dealing with highly sparse …
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 …
Where to go next for recommender systems? id-vs. modality-based recommender models revisited
Recommendation models that utilize unique identities (IDs for short) to represent distinct
users and items have been state-of-the-art (SOTA) and dominated the recommender …
users and items have been state-of-the-art (SOTA) and dominated the recommender …
Leveraging large language models for sequential recommendation
J Harte, W Zorgdrager, P Louridas… - Proceedings of the 17th …, 2023 - dl.acm.org
Sequential recommendation problems have received increasing attention in research during
the past few years, leading to the inception of a large variety of algorithmic approaches. In …
the past few years, leading to the inception of a large variety of algorithmic approaches. In …
Prompt learning for news recommendation
Some recent news recommendation (NR) methods introduce a Pre-trained Language Model
(PLM) to encode news representation by following the vanilla pre-train and fine-tune …
(PLM) to encode news representation by following the vanilla pre-train and fine-tune …
When large language models meet personalization: Perspectives of challenges and opportunities
The advent of large language models marks a revolutionary breakthrough in artificial
intelligence. With the unprecedented scale of training and model parameters, the capability …
intelligence. With the unprecedented scale of training and model parameters, the capability …
Personalized news recommendation: Methods and challenges
Personalized news recommendation is important for users to find interesting news
information and alleviate information overload. Although it has been extensively studied …
information and alleviate information overload. Although it has been extensively studied …
A multi-facet paradigm to bridge large language model and recommendation
Large Language Models (LLMs) have garnered considerable attention in recommender
systems. To achieve LLM-based recommendation, item indexing and generation grounding …
systems. To achieve LLM-based recommendation, item indexing and generation grounding …
Open benchmarking for click-through rate prediction
Click-through rate (CTR) prediction is a critical task for many applications, as its accuracy
has a direct impact on user experience and platform revenue. In recent years, CTR …
has a direct impact on user experience and platform revenue. In recent years, CTR …
MINER: Multi-interest matching network for news recommendation
Personalized news recommendation is an essential technique to help users find interested
news. Accurately matching user's interests and candidate news is the key to news …
news. Accurately matching user's interests and candidate news is the key to news …