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Found 44 papers, 22 papers with code
Plug-in Diffusion Model for Sequential Recommendation
To address this issue, this paper presents a novel Plug-in Diffusion Model for Recommendation (PDRec) framework, which employs the diffusion model as a flexible plugin to jointly take full advantage of the diffusion-generating user preferences on all items.
Distillation is All You Need for Practically Using Different Pre-trained Recommendation Models
Pre-trained recommendation models (PRMs) have attracted widespread attention recently.
AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems
The optimized agents can also propagate their preferences to other agents in subsequent interactions, implicitly capturing the collaborative filtering idea.
Multi-Granularity Click Confidence Learning via Self-Distillation in Recommendation
Due to the lack of supervised signals in click confidence, we first apply self-supervised learning to obtain click confidence scores via a global self-distillation method.
Learning from All Sides: Diversified Positive Augmentation via Self-distillation in Recommendation
A powerful positive item augmentation is beneficial to address the sparsity issue, while few works could jointly consider both the accuracy and diversity of these augmented training labels.
Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender
Graph-based retrieval strategies are inevitably hijacked by heavy users and popular items, leading to the convergence of candidates for users and the lack of system-level diversity.
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach
Inspired by the recent progress on large language models (LLMs), we take a different approach to developing the recommendation models, considering recommendation as instruction following by LLMs.
Triple Sequence Learning for Cross-domain Recommendation
To address this issue, we present a novel framework, termed triple sequence learning for cross-domain recommendation (Tri-CDR), which jointly models the source, target, and mixed behavior sequences to highlight the global and target preference and precisely model the triple correlation in CDR.
Better Pre-Training by Reducing Representation Confusion
Motivated by the above investigation, we propose two novel techniques to improve pre-trained language models: Decoupled Directional Relative Position (DDRP) encoding and MTH pre-training objective.
Reweighting Clicks with Dwell Time in Recommendation
Precisely, we first define a new behavior named valid read, which helps to select high-quality click instances for different users and items via dwell time.
UFNRec: Utilizing False Negative Samples for Sequential Recommendation
To this end, we propose a novel method that can Utilize False Negative samples for sequential Recommendation (UFNRec) to improve model performance.
Multi-granularity Item-based Contrastive Recommendation
Specifically, we build three item-based CL tasks as a set of plug-and-play auxiliary objectives to capture item correlations in feature, semantic and session levels.
Prompt Tuning for Discriminative Pre-trained Language Models
Recent works have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing (NLP) tasks.
Personalized Prompt for Sequential Recommendation
Specifically, we build the personalized soft prefix prompt via a prompt generator based on user profiles and enable a sufficient training of prompts via a prompt-oriented contrastive learning with both prompt- and behavior-based augmentations.
Multi-view Multi-behavior Contrastive Learning in Recommendation
We argue that MBR models should: (1) model the coarse-grained commonalities between different behaviors of a user, (2) consider both individual sequence view and global graph view in multi-behavior modeling, and (3) capture the fine-grained differences between multiple behaviors of a user.
CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation
State-of-the-art sequential recommendation models proposed very recently combine contrastive learning techniques for obtaining high-quality user representations.
Contrastive Cross-domain Recommendation in Matching
Cross-domain recommendation (CDR) aims to provide better recommendation results in the target domain with the help of the source domain, which is widely used and explored in real-world systems.
Personalized Transfer of User Preferences for Cross-domain Recommendation
Specifically, a meta network fed with users' characteristic embeddings is learned to generate personalized bridge functions to achieve personalized transfer of preferences for each user.
Open Hierarchical Relation Extraction
To establish the bidirectional connections between OpenRE and relation hierarchy, we propose the task of open hierarchical relation extraction and present a novel OHRE framework for the task.
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
Besides, MetaHeac has been successfully deployed in WeChat for the promotion of both contents and advertisements, leading to great improvement in the quality of marketing.
Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks
The cold item ID embedding has two main problems: (1) A gap is existing between the cold ID embedding and the deep model.
Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users
With the advantage of meta learning which has good generalization ability to novel tasks, we propose a transfer-meta framework for CDR (TMCDR) which has a transfer stage and a meta stage.
Long Short-Term Temporal Meta-learning in Online Recommendation
An effective online recommendation system should jointly capture users' long-term and short-term preferences in both users' internal behaviors (from the target recommendation task) and external behaviors (from other tasks).
Understanding WeChat User Preferences and "Wow" Diffusion
"Top Stories" is a novel friend-enhanced recommendation engine in WeChat, in which users can read articles based on preferences of both their own and their friends.
Graph Representation Learning
Social and Information Networks
UPRec: User-Aware Pre-training for Recommender Systems
Existing sequential recommendation methods rely on large amounts of training data and usually suffer from the data sparsity problem.
Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network
In this paper, we propose a novel Heterogeneous graph neural network framework for diversified recommendation (GraphDR) in matching to improve both recommendation accuracy and diversity.
Meta-Information Guided Meta-Learning for Few-Shot Relation Classification
Few-shot classification requires classifiers to adapt to new classes with only a few training instances.
Denoising Relation Extraction from Document-level Distant Supervision
Distant supervision (DS) has been widely used to generate auto-labeled data for sentence-level relation extraction (RE), which improves RE performance.
Learning to Build User-tag Profile in Recommendation System (UTPM)
User profiling is one of the most important components in recommendation systems, where a user is profiled using demographic (e. g. gender, age, and location) and user behavior information (e. g. browsing and search history).
Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect
Recommender systems aim to provide item recommendations for users, and are usually faced with data sparsity problem (e. g., cold start) in real-world scenarios.
Towards Fast Adaptation of Neural Architectures with Meta Learning
Recently, Neural Architecture Search (NAS) has been successfully applied to multiple artificial intelligence areas and shows better performance compared with hand-designed networks.
Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction
Specifically, we build a Multi-Relational Item Graph (MRIG) based on all behavior sequences from all sessions, involving target and auxiliary behavior types.
FAQ-based Question Answering via Knowledge Anchors
In this paper, we propose a novel Knowledge Anchor based Question Answering (KAQA) framework for FAQ-based QA to better understand questions and retrieve more appropriate answers.
Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data
Open relation extraction (OpenRE) aims to extract relational facts from the open-domain corpus.
Neural Snowball for Few-Shot Relation Learning
To address new relations with few-shot instances, we propose a novel bootstrapping approach, Neural Snowball, to learn new relations by transferring semantic knowledge about existing relations.
Real-time Attention Based Look-alike Model for Recommender System
Recently, deep learning models play more and more important roles in contents recommender systems.
Atom Responding Machine for Dialog Generation
To enrich the generated responses, ARM introduces a large number of molecule-mechanisms as various responding styles, which are conducted by taking different combinations from a few atom-mechanisms.
COSINE: Compressive Network Embedding on Large-scale Information Networks
There is recently a surge in approaches that learn low-dimensional embeddings of nodes in networks.
Language Modeling with Sparse Product of Sememe Experts
Most language modeling methods rely on large-scale data to statistically learn the sequential patterns of words.
Hierarchical Neural Network for Extracting Knowledgeable Snippets and Documents
Additionally, a "low-level sharing, high-level splitting" structure of CNN is designed to handle the documents from different content domains.
Incorporating Chinese Characters of Words for Lexical Sememe Prediction
However, existing methods of lexical sememe prediction typically rely on the external context of words to represent the meaning, which usually fails to deal with low-frequency and out-of-vocabulary words.
Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence
Experimental results demonstrate that our confidence-aware models achieve significant and consistent improvements on all tasks, which confirms the capability of CKRL modeling confidence with structural information in both KG noise detection and knowledge representation learning.
A Unified Framework for Community Detection and Network Representation Learning
Network representation learning (NRL) aims to learn low-dimensional vectors for vertices in a network.
Social and Information Networks Physics and Society
