The WISE Lab @ Rutgers (rutgerswiselab)

rutgerswiselab

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Company:Rutgers University

Location:New Brunswick, NJ

Home Page:http://wise.cs.rutgers.edu

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The WISE Lab @ Rutgers's repositories

NLR

Code for paper: Neural Logic Reasoning

GenRec

Large Language Model for Generative Recommendation

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NCR

Neural Collaborative Reasoning

user-fairness

User-oriented Fairness in Recommendation

AutoLossGen

AutoLossGen: Automatic Loss Function Generation for Recommender Systems

NS-KGE

Non-Sampling Knowledge Graph Embedding

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DCCF

Deconfounded Causal Collaborative Filtering

DyCCF

Dynamic Causal Collaborative Filtering

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CCF

Causal Collaborative Filtering

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Personalized-Counterfactual-Fairness-in-Recommendation

Code for paper "Personalized Counterfactual Fairness in Recommendation" (a.k.a. "Towards Personalized Fairness based on Causal Notion")

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LLM-RecSys-ID

How to Index Item IDs for Recommendation Foundation Models

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MANAS

Modularized Adaptive Neural Architecture Search

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CAFE

Cafe: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation

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COOKIE

A Dataset for Conversational Recommendation over KnowledgeGraph in E-commerce

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counter

Counterfactual Explainable Recommendation

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EXTRA

SIGIR'21, EXTRA: Explanation Ranking Datasets for Explainable Recommendation

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FCPO

code for ‘Towards Long-term Fairness in Recommendation’

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FedFairRec

Federated Fairness-aware Recommendation

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InstructGLM

Natural Language is All a Graph Needs

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Intent-Aware-Ranking-Transformers

Code on IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems (WWW 2020)

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LOGER

Faithfully Explainable Recommendation via Neural Logic Reasoning

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OpenP5

OpenP5: Benchmarking Foundation Models for Recommendation

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PETER

ACL'21, Personalized Transformer for Explainable Recommendation

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PivotCVAE

This is the implementation code for the WWW2021 paper "Variation Control and Evaluation for Generative Slate Recommendation"

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ucr

Official implementation for paper "User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations" in ECAI2023

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VIP5

VIP5: Towards Multimodal Foundation Models for Recommendation

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