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Found 20 papers, 5 papers with code
Towards Unified Modeling for Positive and Negative Preferences in Sign-Aware Recommendation
Recently, sign-aware graph recommendation has drawn much attention as it will learn users' negative preferences besides positive ones from both positive and negative interactions (i. e., links in a graph) with items.
Repeated Padding as Data Augmentation for Sequential Recommendation
Specifically, we use the original interaction sequences as the padding content and fill it to the padding positions during model training.
Stealthy Attack on Large Language Model based Recommendation
Recently, the powerful large language models (LLMs) have been instrumental in propelling the progress of recommender systems (RS).
Representation Surgery for Multi-Task Model Merging
That is, there is a significant discrepancy in the representation distribution between the merged and individual models, resulting in poor performance of merged MTL.
ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation
In this paper, we revisit the value of ID embeddings for multimodal recommendation and conduct a thorough study regarding its semantics, which we recognize as subtle features of content and structures.
AdaMerging: Adaptive Model Merging for Multi-Task Learning
This approach aims to autonomously learn the coefficients for model merging, either in a task-wise or layer-wise manner, without relying on the original training data.
Continual Learning From a Stream of APIs
Next, we train the CL model by minimizing the gap between the responses of the CL model and the black-box API on synthetic data, to transfer the API's knowledge to the CL model.
Video and Audio are Images: A Cross-Modal Mixer for Original Data on Video-Audio Retrieval
Cross-modal retrieval has become popular in recent years, particularly with the rise of multimedia.
Data Augmented Flatness-aware Gradient Projection for Continual Learning
In this paper, we first revisit the gradient projection method from the perspective of flatness of loss surface, and find that unflatness of the loss surface leads to catastrophic forgetting of the old tasks when the projection constraint is reduced to improve the performance of new tasks.
Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation
However, we observe that the time interval in a sequence may vary significantly different, and thus result in the ineffectiveness of user modeling due to the issue of \emph{preference drift}.
AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learning
In this paper, we propose to measure the task dominance degree of a parameter by the total updates of each task on this parameter.
Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction
The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions.
NEUer at SemEval-2021 Task 4: Complete Summary Representation by Filling Answers into Question for Matching Reading Comprehension
SemEval task 4 aims to find a proper option from multiple candidates to resolve the task of machine reading comprehension.
User-based Network Embedding for Collective Opinion Spammer Detection
Due to the huge commercial interests behind online reviews, a tremendousamount of spammers manufacture spam reviews for product reputation manipulation.
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.
Generalized Embedding Machines for Recommender Systems
In this work, we propose an alternative approach to model high-order interaction signals in the embedding level, namely Generalized Embedding Machine (GEM).
Research Commentary on Recommendations with Side Information: A Survey and Research Directions
This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information.
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.
Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
In the field of sequential recommendation, deep learning (DL)-based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based ones.
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).
