FLIP: Towards Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction

no code implementations • 30 Oct 2023 • Hangyu Wang, Jianghao Lin, Xiangyang Li, Bo Chen, Chenxu Zhu, Ruiming Tang, Weinan Zhang, Yong Yu

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.

Click-Through Rate Prediction Contrastive Learning

ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation

1 code implementation • 22 Aug 2023 • Jianghao Lin, Rong Shan, Chenxu Zhu, Kounianhua Du, Bo Chen, Shigang Quan, Ruiming Tang, Yong Yu, Weinan Zhang

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.

Data Augmentation Language Modelling +3

How Can Recommender Systems Benefit from Large Language Models: A Survey

1 code implementation • 9 Jun 2023 • Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Hao Zhang, Yong liu, Chuhan Wu, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, Weinan Zhang

In this paper, we conduct a comprehensive survey on this research direction from the perspective of the whole pipeline in real-world recommender systems.

Ethics Feature Engineering +5

IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System

2 code implementations • 18 Oct 2022 • Xiangyang Li, Bo Chen, Huifeng Guo, Jingjie Li, Chenxu Zhu, Xiang Long, Sujian Li, Yichao Wang, Wei Guo, Longxia Mao, JinXing Liu, Zhenhua Dong, Ruiming Tang

FE-Block module performs fine-grained and early feature interactions to capture the interactive signals between user and item towers explicitly and CIR module leverages a contrastive interaction regularization to further enhance the interactions implicitly.

QA4PRF: A Question Answering based Framework for Pseudo Relevance Feedback

no code implementations • 16 Nov 2021 • Handong Ma, Jiawei Hou, Chenxu Zhu, Weinan Zhang, Ruiming Tang, Jincai Lai, Jieming Zhu, Xiuqiang He, Yong Yu

Pseudo relevance feedback (PRF) automatically performs query expansion based on top-retrieved documents to better represent the user's information need so as to improve the search results.

Question Answering Semantic Similarity +1

AIM: Automatic Interaction Machine for Click-Through Rate Prediction

1 code implementation • 5 Nov 2021 • Chenxu Zhu, Bo Chen, Weinan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, Yong Yu

To address these three issues mentioned above, we propose Automatic Interaction Machine (AIM) with three core components, namely, Feature Interaction Search (FIS), Interaction Function Search (IFS) and Embedding Dimension Search (EDS), to select significant feature interactions, appropriate interaction functions and necessary embedding dimensions automatically in a unified framework.

Click-Through Rate Prediction

AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction

4 code implementations • 25 Mar 2020 • Bin Liu, Chenxu Zhu, Guilin Li, Wei-Nan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, Yong Yu

By implementing a regularized optimizer over the architecture parameters, the model can automatically identify and remove the redundant feature interactions during the training process of the model.

Click-Through Rate Prediction Recommendation Systems