Recommender systems in the era of large language models (llms)
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys)
have become an important component of our daily life, providing personalized suggestions …
have become an important component of our daily life, providing personalized suggestions …
A survey of graph neural networks for recommender systems: Challenges, methods, and directions
Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …
Recently, graph neural networks have become the new state-of-the-art approach to …
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 …
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 …
News recommendation with attention mechanism
This paper explores the area of news recommendation, a key component of online
information sharing. Initially, we provide a clear introduction to news recommendation …
information sharing. Initially, we provide a clear introduction to news recommendation …
Recommender systems in the era of large language models (llms)
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys)
have become an indispensable and important component in our daily lives, providing …
have become an indispensable and important component in our daily lives, providing …
Mm-rec: Visiolinguistic model empowered multimodal news recommendation
News representation is critical for news recommendation. Most existing methods learn news
representations only from news texts while ignoring the visual information of news. In fact …
representations only from news texts while ignoring the visual information of news. In fact …
A deep neural collaborative filtering based service recommendation method with multi-source data for smart cloud-edge collaboration applications
W Lin, M Zhu, X Zhou, R Zhang, X Zhao… - Tsinghua Science …, 2023 - ieeexplore.ieee.org
Service recommendation provides an effective solution to extract valuable information from
the huge and ever-increasing volume of big data generated by the large cardinality of user …
the huge and ever-increasing volume of big data generated by the large cardinality of user …
Userbert: Pre-training user model with contrastive self-supervision
User modeling is critical for personalization. Existing methods usually train user models from
task-specific labeled data, which may be insufficient. In fact, there are usually abundant …
task-specific labeled data, which may be insufficient. In fact, there are usually abundant …
Is news recommendation a sequential recommendation task?
News recommendation is often modeled as a sequential recommendation task, assuming
there are rich short-term dependencies over historical clicked news. However, users usually …
there are rich short-term dependencies over historical clicked news. However, users usually …