Search Results for author:
Found 49 papers, 26 papers with code
MINER: Multi-Interest Matching Network for News Recommendation
Accurately matching user’s interests and candidate news is the key to news recommendation.
Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation
We validate the effectiveness of PREC through both offline evaluation on public datasets and online A/B testing in an industrial application.
Contrastive Quantization based Semantic Code for Generative Recommendation
Finally, we train and test semantic code with with generative retrieval on a sequential recommendation model.
A Survey on the Memory Mechanism of Large Language Model based Agents
Compared with original LLMs, LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems that need long-term and complex agent-environment interactions.
Recall-Augmented Ranking: Enhancing Click-Through Rate Prediction Accuracy with Cross-Stage Data
RAR consists of two key sub-modules, which synergistically gather information from a vast pool of look-alike users and recall items, resulting in enriched user representations.
PMG : Personalized Multimodal Generation with Large Language Models
Such user preferences are then fed into a generator, such as a multimodal LLM or diffusion model, to produce personalized content.
RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction
Predicting click-through rates (CTR) is a fundamental task for Web applications, where a key issue is to devise effective models for feature interactions.
Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey
The recent advancements in pretrained multimodal models offer new opportunities and challenges in developing content-aware recommender systems.
Discrete Semantic Tokenization for Deep CTR Prediction
Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios.
Unlocking the Potential of Multimodal Unified Discrete Representation through Training-Free Codebook Optimization and Hierarchical Alignment
The Dual Cross-modal Information Disentanglement (DCID) model, utilizing a unified codebook, shows promising results in achieving fine-grained representation and cross-modal generalization.
Benchmarking News Recommendation in the Era of Green AI
Over recent years, news recommender systems have gained significant attention in both academia and industry, emphasizing the need for a standardized benchmark to evaluate and compare the performance of these systems.
Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation
Two-tower models are a prevalent matching framework for recommendation, which have been widely deployed in industrial applications.
Cross-modal Prompts: Adapting Large Pre-trained Models for Audio-Visual Downstream Tasks
This mechanism leverages audio and visual modalities as soft prompts to dynamically adjust the parameters of pre-trained models based on the current multi-modal input features.
Ten Challenges in Industrial Recommender Systems
Huawei's vision and mission is to build a fully connected intelligent world.
Making the Full Model Adaptive: Multi-level Domain Adaptation for Multi-Domain CTR Prediction
We then select them in a domain-aware way to promote informative features for different domains.
Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization
Category information plays a crucial role in enhancing the quality and personalization of recommender systems.
Only Encode Once: Making Content-based News Recommender Greener
Large pretrained language models (PLM) have become de facto news encoders in modern news recommender systems, due to their strong ability in comprehending textual content.
Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction
Click-Through Rate (CTR) prediction, crucial in applications like recommender systems and online advertising, involves ranking items based on the likelihood of user clicks.
DisCover: Disentangled Music Representation Learning for Cover Song Identification
We analyze the CSI task in a disentanglement view with the causal graph technique, and identify the intra-version and inter-version effects biasing the invariant learning.
Exploiting Counter-Examples for Active Learning with Partial labels
In this setting, an oracle annotates the query samples with partial labels, relaxing the oracle from the demanding accurate labeling process.
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.
ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop
The error memory module is designed with fast access capabilities and undergoes continual refreshing with newly observed data samples during the model serving phase to support fast model adaptation.
Denoising Multi-modal Sequential Recommenders with Contrastive Learning
In contrast, modalities that do not cause users' behaviors are potential noises and might mislead the learning of a recommendation model.
FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction
As such, many two-stream interaction models (e. g., DeepFM and DCN) have been proposed by integrating an MLP network with another dedicated network for enhanced CTR prediction.
Ranked #1 on
Click-Through Rate Prediction
on MovieLens
FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation
Item list continuation is proposed to model the overall trend of a list and predict subsequent items.
M4Singer: a Multi-Style, Multi-Singer and Musical Score Provided Mandarin Singing Corpus
The lack of publicly available high-quality and accurately labeled datasets has long been a major bottleneck for singing voice synthesis (SVS).
CCL4Rec: Contrast over Contrastive Learning for Micro-video Recommendation
Micro-video recommender systems suffer from the ubiquitous noises in users' behaviors, which might render the learned user representation indiscriminating, and lead to trivial recommendations (e. g., popular items) or even weird ones that are far beyond users' interests.
BARS: Towards Open Benchmarking for Recommender Systems
Despite significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field.
ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems
Deep learning-based recommendation has become a widely adopted technique in various online applications.
Multi-Level Interaction Reranking with User Behavior History
MIR combines low-level cross-item interaction and high-level set-to-list interaction, where we view the candidate items to be reranked as a set and the users' behavior history in chronological order as a list.
PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation
Specifically, PEAR not only captures feature-level and item-level interactions, but also models item contexts from both the initial ranking list and the historical clicked item list.
Contrastive Learning with Positive-Negative Frame Mask for Music Representation
We devise a novel contrastive learning objective to accommodate both self-augmented positives/negatives sampled from the same music.
Wnet: Audio-Guided Video Object Segmentation via Wavelet-Based Cross-Modal Denoising Networks
Audio-Guided video semantic segmentation is a challenging problem in visual analysis and editing, which automatically separates foreground objects from background in a video sequence according to the referring audio expressions.
QA4PRF: A Question Answering based Framework for Pseudo Relevance Feedback
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.
UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
In this paper, we take one step further to propose an ultra-simplified formulation of GCNs (dubbed UltraGCN), which skips infinite layers of message passing for efficient recommendation.
Ranked #3 on
Recommendation Systems
on Gowalla
Cross-Batch Negative Sampling for Training Two-Tower Recommenders
The two-tower architecture has been widely applied for learning item and user representations, which is important for large-scale recommender systems.
SimpleX: A Simple and Strong Baseline for Collaborative Filtering
While many existing studies focus on the design of more powerful interaction encoders, the impacts of loss functions and negative sampling ratios have not yet been well explored.
Ranked #3 on
Recommendation Systems
on Yelp2018
Why Do We Click: Visual Impression-aware News Recommendation
In this work, inspired by the fact that users make their click decisions mostly based on the visual impression they perceive when browsing news, we propose to capture such visual impression information with visual-semantic modeling for news recommendation.
UNBERT: User-News Matching BERT for News Recommendation
Nowadays, news recommendation has become a popular channel for users to access news of their interests.
Modeling High-order Interactions across Multi-interests for Micro-video Recommendation
Personalized recommendation system has become pervasive in various video platform.
Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding
Weakly-supervised vision-language grounding aims to localize a target moment in a video or a specific region in an image according to the given sentence query, where only video-level or image-level sentence annotations are provided during training.
Ensemble Knowledge Distillation for CTR Prediction
Recently, deep learning-based models have been widely studied for click-through rate (CTR) prediction and lead to improved prediction accuracy in many industrial applications.
BARS-CTR: Open Benchmarking for Click-Through Rate Prediction
We have publicly released the benchmarking code, evaluation protocols, and hyper-parameter settings of our work to promote reproducible research in this field.
Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach
In this work, we propose to formulate item tagging as a link prediction problem between item nodes and tag nodes.
Regularized Two-Branch Proposal Networks for Weakly-Supervised Moment Retrieval in Videos
Thus, these methods fail to distinguish the target moment from plausible negative moments.
Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics
To fill this significant gap and facilitate more research on AI-driven log analytics, we have collected and released loghub, a large collection of system log datasets.
Software Engineering
Logzip: Extracting Hidden Structures via Iterative Clustering for Log Compression
Data compression is essential to reduce the cost of log storage.
Databases Software Engineering
Tools and Benchmarks for Automated Log Parsing
Logs are imperative in the development and maintenance process of many software systems.
Software Engineering
A Directed Acyclic Graph Approach to Online Log Parsing
A typical log-based system reliability management procedure is to first parse log messages because of their unstructured format; and apply data mining techniques on the parsed logs to obtain critical system behavior information.
Software Engineering
