Search Results for author:
Found 31 papers, 19 papers with code
Offline Trajectory Generalization for Offline Reinforcement Learning
Then we propose four strategies to use World Transformers to generate high-rewarded trajectory simulation by perturbing the offline data.
Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviors
However, we argue that a critical obstacle remains in deploying LLMs for practical use: the efficiency of LLMs when processing long textual user behaviors.
Empowering Sequential Recommendation from Collaborative Signals and Semantic Relatedness
It is also significant to model the \textit{semantic relatedness} reflected in content features, e. g., images and text.
Multi-Modality is All You Need for Transferable Recommender Systems
To align the cross-modal item representations, we propose a novel next-item enhanced cross-modal contrastive learning objective, which is equipped with both inter- and intra-modality negative samples and explicitly incorporates the transition patterns of user behaviors into the item encoders.
Hypergraph Node Representation Learning with One-Stage Message Passing
HGraphormer injects the hypergraph structure information (local information) into Transformers (global information) by combining the attention matrix and hypergraph Laplacian.
A Content-Driven Micro-Video Recommendation Dataset at Scale
Micro-videos have recently gained immense popularity, sparking critical research in micro-video recommendation with significant implications for the entertainment, advertising, and e-commerce industries.
NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation
The development of TransRec has encountered multiple challenges, among which the lack of large-scale, high-quality transfer learning recommendation dataset and benchmark suites is one of the biggest obstacles.
An Image Dataset for Benchmarking Recommender Systems with Raw Pixels
Then, to show the effectiveness of the dataset's image features, we substitute the itemID embeddings (from IDNet) with a powerful vision encoder that represents items using their raw image pixels.
Ranked #1 on
Recommendation Systems
on PixelRec
Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights
If yes, we benchmark these existing adapters, which have been shown to be effective in NLP and CV tasks, in item recommendation tasks.
Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights
Furthermore, we compare the performance of the TCF paradigm utilizing the most powerful LMs to the currently dominant ID embedding-based paradigm and investigate the transferability of this TCF paradigm.
Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited
In fact, this question was answered ten years ago when IDRec beats MoRec by a strong margin in both recommendation accuracy and efficiency.
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback.
Exploring evolution-aware & -free protein language models as protein function predictors
Large-scale Protein Language Models (PLMs) have improved performance in protein prediction tasks, ranging from 3D structure prediction to various function predictions.
TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback
That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms.
Enhancing Top-N Item Recommendations by Peer Collaboration
In this paper, we plan to exploit such redundancy phenomena to improve the performance of RS.
Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking
This ability to learn consecutive tasks without forgetting how to perform previously trained problems is essential for developing an online dialogue system.
Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search
To realize such a goal, we propose AdaRec, a knowledge distillation (KD) framework which compresses knowledge of a teacher model into a student model adaptively according to its recommendation scene by using differentiable Neural Architecture Search (NAS).
Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification
In this paper, we propose a novel iterative network pruning with uncertainty regularization method for lifelong sentiment classification (IPRLS), which leverages the principles of network pruning and weight regularization.
User-specific Adaptive Fine-tuning for Cross-domain Recommendations
Fine-tuning works as an effective transfer learning technique for this objective, which adapts the parameters of a pre-trained model from the source domain to the target domain.
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking
We validate the performance of StackRec by instantiating it with four state-of-the-art SR models in three practical scenarios with real-world datasets.
One Person, One Model, One World: Learning Continual User Representation without Forgetting
In this paper, we delve on research to continually learn user representations task by task, whereby new tasks are learned while using partial parameters from old ones.
CmnRec: Sequential Recommendations with Chunk-accelerated Memory Network
Specifically, our framework divides proximal information units into chunks, and performs memory access at certain time steps, whereby the number of memory operations can be greatly reduced.
A Generic Network Compression Framework for Sequential Recommender Systems
Current state-of-the-art sequential recommender models are typically based on a sandwich-structured deep neural network, where one or more middle (hidden) layers are placed between the input embedding layer and output softmax layer.
Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
To overcome this issue, we develop a parameter efficient transfer learning architecture, termed as PeterRec, which can be configured on-the-fly to various downstream tasks.
Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering
In this work, we emphasize on modeling the correlations among embedding dimensions in neural networks to pursue higher effectiveness for CF.
Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation
To capture the sequential dependencies, existing methods resort either to data augmentation techniques or left-to-right style autoregressive training. Since these methods are aimed to model the sequential nature of user behaviors, they ignore the future data of a target interaction when constructing the prediction model for it.
Adversarial Training Towards Robust Multimedia Recommender System
To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning.
Information Retrieval Multimedia
A Simple Convolutional Generative Network for Next Item Recommendation
Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation.
Batch IS NOT Heavy: Learning Word Representations From All Samples
Stochastic Gradient Descent (SGD) with negative sampling is the most prevalent approach to learn word representations.
VSE-ens: Visual-Semantic Embeddings with Efficient Negative Sampling
Jointing visual-semantic embeddings (VSE) have become a research hotpot for the task of image annotation, which suffers from the issue of semantic gap, i. e., the gap between images' visual features (low-level) and labels' semantic features (high-level).
Improving Negative Sampling for Word Representation using Self-embedded Features
Although the word-popularity based negative sampler has shown superb performance in the skip-gram model, the theoretical motivation behind oversampling popular (non-observed) words as negative samples is still not well understood.
