Search Results for author:
Found 30 papers, 11 papers with code
Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension
Word sense disambiguation (WSD), identifying the most suitable meaning of ambiguous words in the given contexts according to a predefined sense inventory, is one of the most classical and challenging tasks in natural language processing.
Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation
Intriguingly, the conclusion drawn from our study is that, certain data augmentation strategies can achieve similar or even superior performance compared with some CL-based methods, demonstrating the potential to significantly alleviate the data sparsity issue with fewer computational overhead.
MSynFD: Multi-hop Syntax aware Fake News Detection
These existing methods fail to handle the complex, subtle twists in news articles, such as syntax-semantics mismatches and prior biases, leading to lower performance and potential failure when modalities or social context are missing.
Causal Learning for Trustworthy Recommender Systems: A Survey
However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS).
An empirical study of next-basket recommendations
This methodological rigor establishes a cohesive framework for the impartial evaluation of diverse NBR approaches.
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
FreTS mainly involves two stages, (i) Domain Conversion, that transforms time-domain signals into complex numbers of frequency domain; (ii) Frequency Learning, that performs our redesigned MLPs for the learning of real and imaginary part of frequency components.
LLMRec: Benchmarking Large Language Models on Recommendation Task
The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
Attention Calibration for Transformer-based Sequential Recommendation
Our empirical analysis of some representative Transformer-based SR models reveals that it is not uncommon for large attention weights to be assigned to less relevant items, which can result in inaccurate recommendations.
Causal Intervention for Abstractive Related Work Generation
Abstractive related work generation has attracted increasing attention in generating coherent related work that better helps readers grasp the background in the current research.
Rumor Detection with Hierarchical Representation on Bipartite Adhoc Event Trees
Accordingly, we propose a novel rumor detection model with hierarchical representation on the bipartite adhoc event trees called BAET.
Medical Question Summarization with Entity-driven Contrastive Learning
Although existing works have attempted to utilize Seq2Seq, reinforcement learning, or contrastive learning to solve the problem, two challenges remain: how to correctly capture question focus to model its semantic intention, and how to obtain reliable datasets to fairly evaluate performance.
A Survey on Deep Learning based Time Series Analysis with Frequency Transformation
Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT.
A Counterfactual Collaborative Session-based Recommender System
Next, COCO-SBRS adopts counterfactual inference to recommend items based on the outputs of the pre-trained recommendation model considering the causalities to alleviate the data sparsity problem.
Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective
To bridge such significant gap, we formulate the fairness modeling problem as learning informative representations attending to both advantaged and disadvantaged variables.
Equivariant Contrastive Learning for Sequential Recommendation
Therefore, we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations (e. g., item substitution) and insensitive to mild augmentations (e. g., featurelevel dropout masking).
A Systematical Evaluation for Next-Basket Recommendation Algorithms
Different studies often evaluate NBR approaches on different datasets, under different experimental settings, making it hard to fairly and effectively compare the performance of different NBR approaches.
An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters
Story ending generation aims at generating reasonable endings for a given story context.
Trustworthy Recommender Systems
However, researchers lack a systematic overview and discussion of the literature in this novel and fast developing field of TRSs.
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation
Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis.
Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities
In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations.
Aspect-driven User Preference and News Representation Learning for News Recommendation
News recommender systems are essential for helping users to efficiently and effectively find out those interesting news from a large amount of news.
Next-item Recommendations in Short Sessions
The obtained similar sessions are then utilized to complement and optimize the preference representation learned from the current short session by the local module for more accurate next-item recommendations in this short session.
Graph Learning based Recommender Systems: A Review
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).
Stratified and Time-aware Sampling based Adaptive Ensemble Learning for Streaming Recommendations
To address these problems, we propose a Stratified and Time-aware Sampling based Adaptive Ensemble Learning framework, called STS-AEL, to improve the accuracy of streaming recommendations.
Double-Wing Mixture of Experts for Streaming Recommendations
In VRS-DWMoE, we first devise variational and reservoir-enhanced sampling to wisely complement new data with historical data, and thus address the user preference drift issue while capturing long-term user preferences.
Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation
Most existing TBRSs recommend next item by only modeling the intra-transaction dependency within the current transaction while ignoring inter-transaction dependency with recent transactions that may also affect the next item.
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation
To mitigate this shortcoming, we propose a novel group equivariant segmentation framework by encoding those inherent symmetries for learning more precise representations.
Graph Learning Approaches to Recommender Systems: A Review
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).
Sequential Recommender Systems: Challenges, Progress and Prospects
The emerging topic of sequential recommender systems has attracted increasing attention in recent years. Different from the conventional recommender systems including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users preferences and item popularity over time.
A Survey on Session-based Recommender Systems
In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs.
