Search Results for author:
Found 15 papers, 11 papers with code
M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation
Furthermore, through causal graph analysis, we have discovered that the scenario itself directly influences click behavior, yet existing approaches directly incorporate data from other scenarios during the training of the current scenario, leading to prediction biases when they directly utilize click behaviors from other scenarios to train models.
Play to Your Strengths: Collaborative Intelligence of Conventional Recommender Models and Large Language Models
The rise of large language models (LLMs) has opened new opportunities in Recommender Systems (RSs) by enhancing user behavior modeling and content understanding.
Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models
Sequential recommendation aims to estimate the dynamic user preferences and sequential dependencies among historical user behaviors.
Towards Efficient and Effective Unlearning of Large Language Models for Recommendation
However, in order to protect user privacy and optimize utility, it is also crucial for LLMRec to intentionally forget specific user data, which is generally referred to as recommendation unlearning.
FLIP: Towards Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction
Specifically, the masked data of one modality (i. e., tokens or features) has to be recovered with the help of the other modality, which establishes the feature-level interaction and alignment via sufficient mutual information extraction between dual modalities.
ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction
Traditional CTR models convert the multi-field categorical data into ID features via one-hot encoding, and extract the collaborative signals among features.
CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models
With the emergence of Large Language Models (LLMs), there has been a significant improvement in the programming capabilities of models, attracting growing attention from researchers.
ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation
With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and have been actively explored currently.
MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction
The large capacity of neural models helps digest such massive amounts of data under the supervised learning paradigm, yet they fail to utilize the substantial data to its full potential, since the 1-bit click signal is not sufficient to guide the model to learn capable representations of features and instances.
Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models
In this work, we propose an Open-World Knowledge Augmented Recommendation Framework with Large Language Models, dubbed KAR, to acquire two types of external knowledge from LLMs -- the reasoning knowledge on user preferences and the factual knowledge on items.
How Can Recommender Systems Benefit from Large Language Models: A Survey
In this paper, we conduct a comprehensive survey on this research direction from the perspective of the whole pipeline in real-world recommender systems.
A Bird's-eye View of Reranking: from List Level to Page Level
Moreover, simply applying a shared network for all the lists fails to capture the commonalities and distinctions in user behaviors on different lists.
A Graph-Enhanced Click Model for Web Search
To better exploit search logs and model users' behavior patterns, numerous click models are proposed to extract users' implicit interaction feedback.
An F-shape Click Model for Information Retrieval on Multi-block Mobile Pages
However, with the development of user interface (UI) design, the layout of displayed items on a result page tends to be multi-block (i. e., multi-list) style instead of a single list, which requires different assumptions to model user behaviors more accurately.
An Adversarial Imitation Click Model for Information Retrieval
Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback.
