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Found 19 papers, 8 papers with code
Augmenting Legal Judgment Prediction with Contrastive Case Relations
Existing legal judgment prediction methods usually only consider one single case fact description as input, which may not fully utilize the information in the data such as case relations and frequency.
Weighted self Distillation for Chinese word segmentation
Recent researches show that multi-criteria resources and n-gram features are beneficial to Chinese Word Segmentation (CWS).
Lossless and Privacy-Preserving Graph Convolution Network for Federated Item Recommendation
However, due to privacy constraints, the graph convolution process in existing federated recommendation methods is incomplete compared with the centralized counterpart, causing a degradation of the recommendation performance.
Sample Enrichment via Temporary Operations on Subsequences for Sequential Recommendation
Sequential recommendation leverages interaction sequences to predict forthcoming user behaviors, crucial for crafting personalized recommendations.
A Practice-Friendly LLM-Enhanced Paradigm with Preference Parsing for Sequential Recommendation
Specifically, in the information reconstruction stage, we design a new user-level SFT task for collaborative information injection with the assistance of a pre-trained SRS model, which is more efficient and compatible with limited text information.
BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation
In real recommendation scenarios, users often have different types of behaviors, such as clicking and buying.
Privacy-Preserving Cross-Domain Sequential Recommendation
It combines the characteristics of sequential recommender systems and cross-domain recommender systems, which can capture the dynamic preferences of users and alleviate the problem of cold-start users.
A Survey on Cross-Domain Sequential Recommendation
Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple domains at different granularities (ranging from inter-sequence to intra-sequence and from single-domain to cross-domain).
A Survey on Multi-Behavior Sequential Recommendation
MBSR is a relatively new and worthy direction for in-depth research, which can achieve state-of-the-art recommendation through suitable modeling, and some related works have been proposed.
GNN4FR: A Lossless GNN-based Federated Recommendation Framework
Graph neural networks (GNNs) have gained wide popularity in recommender systems due to their capability to capture higher-order structure information among the nodes of users and items.
Bounding System-Induced Biases in Recommender Systems with A Randomized Dataset
To bridge this gap, we study the debiasing problem from a new perspective and propose to directly minimize the upper bound of an ideal objective function, which facilitates a better potential solution to the system-induced biases.
Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation
Specifically, SSTE uses a self-sampling module to generate some subsets with different degrees of bias from the original training and validation data.
An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature Selection
Thus, this paper devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer to efficiently solve these related tasks via knowledge transfer.
DIWIFT: Discovering Instance-wise Influential Features for Tabular Data
Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce.
Abstraction and Refinement: Towards Scalable and Exact Verification of Neural Networks
Our approach is orthogonal to and can be integrated with many existing verification techniques.
PAS: A Position-Aware Similarity Measurement for Sequential Recommendation
The common item-based collaborative filtering framework becomes a typical recommendation method when equipped with a certain item-to-item similarity measurement.
Diversifying Agent's Behaviors in Interactive Decision Models
In this article, we investigate into diversifying behaviors of other agents in the subject agent's decision model prior to their interactions.
Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices
In this paper, we propose an enhanced deep learning framework for IoT device identification using physical layer signals.
A Biologically Inspired Feature Enhancement Framework for Zero-Shot Learning
Most of the Zero-Shot Learning (ZSL) algorithms currently use pre-trained models as their feature extractors, which are usually trained on the ImageNet data set by using deep neural networks.
