Papers Interactive Recommendation
“Interactive Recommendation” 태그가 달린 논문 40편 · 필터 해제
Thought-Augmented Planning for LLM-Powered Interactive Recommender Agent
Interactive recommendation is a typical information-seeking task that allows users to interactively express their needs through natural language and obtain personalized recommendations. Large language model-powered (LLM-…
Interactive RecommendationLarge Language ModelRecommendation SystemsUser SimulationContrastive Representation for Interactive Recommendation
Interactive Recommendation (IR) has gained significant attention recently for its capability to quickly capture dynamic interest and optimize both short and long term objectives. IR agents are typically implemented throu…
Contrastive LearningDeep Reinforcement LearningInteractive RecommendationRecommendation SystemsInteraRec: Screenshot Based Recommendations Using Multimodal Large Language Models
Weblogs, comprised of records detailing user activities on any website, offer valuable insights into user preferences, behavior, and interests. Numerous recommendation algorithms, employing strategies such as collaborati…
Collaborative FilteringInteractive RecommendationNavigateRecommendation Systems+1Debiased Model-based Interactive Recommendation
Existing model-based interactive recommendation systems are trained by querying a world model to capture the user preference, but learning the world model from historical logged data will easily suffer from bias issues s…
Contrastive LearningInteractive RecommendationmodelRecommendation SystemsA General Neural Causal Model for Interactive Recommendation
Survivor bias in observational data leads the optimization of recommender systems towards local optima. Currently most solutions re-mines existing human-system collaboration patterns to maximize longer-term satisfaction …
counterfactualCounterfactual InferenceInteractive RecommendationRecommendation SystemsAdversarial Batch Inverse Reinforcement Learning: Learn to Reward from Imperfect Demonstration for Interactive Recommendation
Rewards serve as a measure of user satisfaction and act as a limiting factor in interactive recommender systems. In this research, we focus on the problem of learning to reward (LTR), which is fundamental to reinforcemen…
Interactive RecommendationRecommendation Systemsreinforcement-learningReinforcement LearningPreference Elicitation with Soft Attributes in Interactive Recommendation
Preference elicitation plays a central role in interactive recommender systems. Most preference elicitation approaches use either item queries that ask users to select preferred items from a slate, or attribute queries t…
AttributeInteractive RecommendationRecommendation SystemsA General Offline Reinforcement Learning Framework for Interactive Recommendation
This paper studies the problem of learning interactive recommender systems from logged feedbacks without any exploration in online environments. We address the problem by proposing a general offline reinforcement learnin…
Interactive RecommendationRecommendation Systemsreinforcement-learningReinforcement LearningTowards Validating Long-Term User Feedbacks in Interactive Recommendation Systems
Interactive Recommender Systems (IRSs) have attracted a lot of attention, due to their ability to model interactive processes between users and recommender systems. Numerous approaches have adopted Reinforcement Learning…
Interactive RecommendationRecommendation SystemsReinforcement Learning (RL)Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
Offline reinforcement learning (RL), a technology that offline learns a policy from logged data without the need to interact with online environments, has become a favorable choice in decision-making processes like inter…
Decision MakingInteractive RecommendationOffline RLRecommendation Systems+3Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation
In dynamic interaction graphs, user-item interactions usually follow heterogeneous patterns, represented by different structural information, such as user-item co-occurrence, sequential information of user interactions a…
Collaborative FilteringInteractive RecommendationDigital Human Interactive Recommendation Decision-Making Based on Reinforcement Learning
Digital human recommendation system has been developed to help customers find their favorite products and is playing an active role in various recommendation contexts. How to timely catch and learn the dynamics of the pr…
Decision MakingGraph EmbeddingInteractive Recommendationreinforcement-learning+2Dynamic Global Sensitivity for Differentially Private Contextual Bandits
Bandit algorithms have become a reference solution for interactive recommendation. However, as such algorithms directly interact with users for improved recommendations, serious privacy concerns have been raised regardin…
Interactive RecommendationMulti-Armed BanditsSensitivityKuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
Recommender systems deployed in real-world applications can have inherent exposure bias, which leads to the biased logged data plaguing the researchers. A fundamental way to address this thorny problem is to collect user…
Interactive RecommendationMulti-Task LearningRecommendation SystemsSequential RecommendationContrastive Learning for Interactive Recommendation in Fashion
Recommender systems and search are both indispensable in facilitating personalization and ease of browsing in online fashion platforms. However, the two tools often operate independently, failing to combine the strengths…
Contrastive LearningInteractive RecommendationRecommendation SystemsRetrievalModelling Users with Item Metadata for Explainable and Interactive Recommendation
Recommender systems are used in many different applications and contexts, however their main goal can always be summarised as "connecting relevant content to interested users". Personalized recommendation algorithms achi…
Collaborative FilteringInteractive RecommendationRecommendation SystemsCIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System
While personalization increases the utility of recommender systems, it also brings the issue of filter bubbles. E.g., if the system keeps exposing and recommending the items that the user is interested in, it may also ma…
Causal InferencecounterfactualInteractive RecommendationOffline RL+1Model-agnostic Counterfactual Synthesis Policy for Interactive Recommendation
Interactive recommendation is able to learn from the interactive processes between users and systems to confront the dynamic interests of users. Recent advances have convinced that the ability of reinforcement learning t…
counterfactualInteractive Recommendationmodelreinforcement-learning+1Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems
Adversarial attacks, e.g., adversarial perturbations of the input and adversarial samples, pose significant challenges to machine learning and deep learning techniques, including interactive recommendation systems. The l…
Adversarial RobustnesscounterfactualDeep Reinforcement LearningInteractive Recommendation+4D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning
Recently, interactive recommendation systems based on reinforcement learning have been attended by researchers due to the consider recommendation procedure as a dynamic process and update the recommendation model based o…
Deep Reinforcement LearningDiversityInteractive RecommendationRecommendation Systems+3