As the focus on Large Language Models (LLMs) in the field of recommendation intensifies,
the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a …

Recommendation unlearning is an emerging task to serve users for erasing unusable data
(eg, some historical behaviors) from a well-trained recommender model. Existing methods …

Sequential recommender systems often struggle with accurate personalized
recommendations due to data sparsity issues. Existing works use variational autoencoders …

Dynamic sequential recommendation (DSR) can generate model parameters based on user
behavior to improve the personalization of sequential recommendation under various user …

Adapting Large Language Models for Recommendation (LLM4Rec) has shown promising
results. However, the challenges of deploying LLM4Rec in real-world scenarios remain …

Large Language Models (LLMs) have shown impressive performance in various domains,
prompting researchers to explore their potential application in recommendation systems …

The evolving paradigm of Large Language Model-based Recom-mendation (LLMRec)
customizes Large Language Models (LLMs) through parameter-efficient fine-tuning (PEFT) …

Click-through rate (CTR) prediction plays an important role in online advertising platforms.
Most existing methods use data from the advertising platform itself for CTR prediction. As …

Knowledge graph (KG) demonstrates substantial potential for enhancing the performance of
recommender systems. Due to its rich semantic content and associations among interactive …

Recommender systems require the simultaneous optimization of multiple objectives to
accurately model user interests, necessitating the application of multi-task learning methods …