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Found 36 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.
IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT
This is also a notable improvement over the Adapter and LoRA, which require 37-39 GB GPU memory and 350-380 seconds per epoch for training.
Enhanced Generative Recommendation via Content and Collaboration Integration
However, existing generative recommendation approaches still encounter challenges in (i) effectively integrating user-item collaborative signals and item content information within a unified generative framework, and (ii) executing an efficient alignment between content information and collaborative signals.
Uncovering Selective State Space Model's Capabilities in Lifelong Sequential Recommendation
We conduct extensive experiments to evaluate the performance of representative sequential recommendation models in the setting of lifelong sequences.
On the Effectiveness of Unlearning in Session-Based Recommendation
On the one hand, these approaches cannot achieve satisfying unlearning effects due to the collaborative correlations and sequential connections between the unlearning item and the remaining items in the session.
Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure
Then, we introduce a penalty to items with high exposure probability to avoid the overestimation of user preference for biased samples.
Learning Robust Sequential Recommenders through Confident Soft Labels
CSRec contains a teacher module that generates high-quality and confident soft labels and a student module that acts as the target recommender and is trained on the combination of dense, soft labels and sparse, one-hot labels.
Distributed end-effector formation control for mixed fully- and under-actuated manipulators with flexible joints
The presence of faulty or underactuated manipulators can disrupt the end-effector formation keeping of a team of manipulators.
Distributed formation control of end-effector of mixed planar fully- and under-actuated manipulators
This paper addresses the problem of end-effector formation control for a mixed group of two-link manipulators moving in a horizontal plane that comprises of fully-actuated manipulators and underactuated manipulators with only the second joint being actuated (referred to as the passive-active (PA) manipulators).
Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extending the capability of LLMs.
Label Denoising through Cross-Model Agreement
We employ the proposed DeCA on both the binary label scenario and the multiple label scenario.
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
How Graph Convolutions Amplify Popularity Bias for Recommendation?
Through theoretical analyses, we identify two fundamental factors: (1) with graph convolution (\textit{i. e.,} neighborhood aggregation), popular items exert larger influence than tail items on neighbor users, making the users move towards popular items in the representation space; (2) after multiple times of graph convolution, popular items would affect more high-order neighbors and become more influential.
Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems
For the second issue, we propose introducing contrastive signals between augmented states and the state randomly sampled from other sessions to improve the state representation learning further.
Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target behavior such as purchases.
A Self-Correcting Sequential Recommender
STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items.
Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation
We propose a variational Bayesian method to approximate posterior distributions over dialogue-specific subgraphs, which not only leverages the dialogue corpus for restructuring missing entity relations but also dynamically selects knowledge based on the dialogue context.
On the User Behavior Leakage from Recommender System Exposure
Given the fact that system exposure data could be widely accessed from a relatively larger scope, we believe that the user past behavior privacy has a high risk of leakage in recommender systems.
Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective
As reinforcement learning (RL) naturally fits this objective -- maximizing an user's reward per session -- it has become an emerging topic in recommender systems.
GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation
However, existing graph-based methods fails to consider the bias offsets of users (items).
Supervised Advantage Actor-Critic for Recommender Systems
However, the direct use of RL algorithms in the RS setting is impractical due to challenges like off-policy training, huge action spaces and lack of sufficient reward signals.
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
The proposed SMORL agent augments standard recommendation models with additional RL layers that enforce it to simultaneously satisfy three principal objectives: accuracy, diversity, and novelty of recommendations.
Multi-Objective Reinforcement Learning
reinforcement-learning
+2
Extracting Attentive Social Temporal Excitation for Sequential Recommendation
In this paper, we propose a novel time-aware sequential recommendation framework called Social Temporal Excitation Networks (STEN), which introduces temporal point processes to model the fine-grained impact of friends' behaviors on the user s dynamic interests in an event-level direct paradigm.
ICPE: An Item Cluster-Wise Pareto-Efficient Framework for Recommendation Debiasing
In detail, we define our item cluster-wise optimization target as the recommender model should balance all item clusters that differ in popularity, thus we set the model learning on each item cluster as a unique optimization objective.
ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues
(1) there is no dataset with large-scale medical dialogues that covers multiple medical services and contains fine-grained medical labels (i. e., intents, actions, slots, values), and (2) there is no set of established benchmarks for MDSs for multi-domain, multi-service medical dialogues.
N-ary Constituent Tree Parsing with Recursive Semi-Markov Model
In this paper, we study the task of graph-based constituent parsing in the setting that binarization is not conducted as a pre-processing step, where a constituent tree may consist of nodes with more than two children.
Ranked #3 on
Constituency Parsing
on CTB5
Learning Robust Recommenders through Cross-Model Agreement
A noisy negative example which is uninteracted because of unawareness of the user could also denote potential positive user preference.
AutoDebias: Learning to Debias for Recommendation
This provides a valuable opportunity to develop a universal solution for debiasing, e. g., by learning the debiasing parameters from data.
Graph Convolutional Embeddings for Recommender Systems
In this work, we generalize the use of GCNs for N-partite graphs by considering N multiple context dimensions and propose a simple way for their seamless integration in modern deep learning RS architectures.
Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method
To this end, we analyze the working mechanism of GCN with causal graph, estimating the causal effect of a node's local structure for the prediction.
Self-Supervised Reinforcement Learning for Recommender Systems
A major component of RL approaches is to train the agent through interactions with the environment.
Graph Highway Networks
Graph Convolution Networks (GCN) are widely used in learning graph representations due to their effectiveness and efficiency.
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).
Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation
In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system.
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.
Pulsar Candidate Identification with Artificial Intelligence Techniques
To address these problems, we proposed a framework which combines deep convolution generative adversarial network (DCGAN) with support vector machine (SVM) to deal with imbalance class problem and to improve pulsar identification accuracy.
