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 …
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 …
Transrec: Learning transferable recommendation from mixture-of-modality feedback
As multimedia systems like Tiktok and Youtube become increasingly prevalent, there is a
growing demand for effective recommendation techniques. However, current …
growing demand for effective recommendation techniques. However, current …
Ninerec: A benchmark dataset suite for evaluating transferable recommendation
Large foundational models, through upstream pre-training and downstream fine-tuning,
have achieved immense success in the broad AI community due to improved model …
have achieved immense success in the broad AI community due to improved model …
Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language
With the thriving of pre-trained language model (PLM) widely verified in various of NLP
tasks, pioneer efforts attempt to explore the possible cooperation of the general textual …
tasks, pioneer efforts attempt to explore the possible cooperation of the general textual …
IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT
Multimodal foundation models are transformative in sequential recommender systems,
leveraging powerful representation learning capabilities. While Parameter-efficient Fine …
leveraging powerful representation learning capabilities. While Parameter-efficient Fine …
Collaborative Word-based Pre-trained Item Representation for Transferable Recommendation
Item representation learning (IRL) plays an essential role in recommender systems,
especially for sequential recommendation. Traditional sequential recommendation models …
especially for sequential recommendation. Traditional sequential recommendation models …
Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation
Cross-domain Recommendation (CDR) as one of the effective techniques in alleviating the
data sparsity issues has been widely studied in recent years. However, previous works may …
data sparsity issues has been widely studied in recent years. However, previous works may …
TBIN: Modeling Long Textual Behavior Data for CTR Prediction
S Chen, X Li, J Dong, J Zhang, Y Wang… - arXiv preprint arXiv …, 2023 - arxiv.org
Click-through rate (CTR) prediction plays a pivotal role in the success of recommendations.
Inspired by the recent thriving of language models (LMs), a surge of works improve …
Inspired by the recent thriving of language models (LMs), a surge of works improve …
Scaling Sequential Recommendation Models with Transformers
Modeling user preferences has been mainly addressed by looking at users' interaction
history with the different elements available in the system. Tailoring content to individual …
history with the different elements available in the system. Tailoring content to individual …