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 …
Large language models are zero-shot rankers for recommender systems
Recently, large language models (LLMs)(eg, GPT-4) have demonstrated impressive general-
purpose task-solving abilities, including the potential to approach recommendation tasks …
purpose task-solving abilities, including the potential to approach recommendation tasks …
Towards universal sequence representation learning for recommender systems
In order to develop effective sequential recommenders, a series of sequence representation
learning (SRL) methods are proposed to model historical user behaviors. Most existing SRL …
learning (SRL) methods are proposed to model historical user behaviors. Most existing SRL …
Recommendation as instruction following: A large language model empowered recommendation approach
In the past decades, recommender systems have attracted much attention in both research
and industry communities, and a large number of studies have been devoted to developing …
and industry communities, and a large number of studies have been devoted to developing …
Text is all you need: Learning language representations for sequential recommendation
Sequential recommendation aims to model dynamic user behavior from historical
interactions. Existing methods rely on either explicit item IDs or general textual features for …
interactions. Existing methods rely on either explicit item IDs or general textual features for …
Towards open-world recommendation with knowledge augmentation from large language models
Recommender systems play a vital role in various online services. However, the insulated
nature of training and deploying separately within a specific domain limits their access to …
nature of training and deploying separately within a specific domain limits their access to …
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 …
Learning vector-quantized item representation for transferable sequential recommenders
Recently, the generality of natural language text has been leveraged to develop transferable
recommender systems. The basic idea is to employ pre-trained language models (PLM) to …
recommender systems. The basic idea is to employ pre-trained language models (PLM) to …
Adapting large language models by integrating collaborative semantics for recommendation
Recently, large language models (LLMs) have shown great potential in recommender
systems, either improving existing recommendation models or serving as the backbone …
systems, either improving existing recommendation models or serving as the backbone …
Linkless link prediction via relational distillation
Abstract Graph Neural Networks (GNNs) have shown exceptional performance in the task of
link prediction. Despite their effectiveness, the high latency brought by non-trivial …
link prediction. Despite their effectiveness, the high latency brought by non-trivial …