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Found 36 papers, 21 papers with code
Improving Personalized Explanation Generation through Visualization
In modern recommender systems, there are usually comments or reviews from users that justify their ratings for different items.
Towards LLM-RecSys Alignment with Textual ID Learning
The results show that the zero-shot performance of the pre-trained foundation model is comparable to or even better than some traditional recommendation models based on supervised training, showing the potential of the IDGen paradigm serving as the foundation model for generative recommendation.
AIOS: LLM Agent Operating System
Inspired by these challenges, this paper presents AIOS, an LLM agent operating system, which embeds large language model into operating systems (OS) as the brain of the OS, enabling an operating system "with soul" -- an important step towards AGI.
PAP-REC: Personalized Automatic Prompt for Recommendation Language Model
In this paper, we propose PAP-REC, a framework to generate the Personalized Automatic Prompt for RECommendation language models to mitigate the inefficiency and ineffectiveness problems derived from manually designed prompts.
LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystem
We envision that LLM's impact will not be limited to the AI application level, instead, it will in turn revolutionize the design and implementation of computer system, architecture, software, and programming language, featured by several main concepts: LLM as OS (system-level), Agents as Applications (application-level), Natural Language as Programming Interface (user-level), and Tools as Devices/Libraries (hardware/middleware-level).
War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars
Can we avoid wars at the crossroads of history?
User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations
Acknowledging the recent advancements in explainable recommender systems that enhance users' understanding of recommendation mechanisms, we propose leveraging these advancements to improve user controllability.
GenRec: Large Language Model for Generative Recommendation
Subsequently, we use these prompts to fine-tune the LLaMA backbone LLM on a dataset of user-item interactions, represented by textual data, to capture user preferences and item characteristics.
Counterfactual Collaborative Reasoning
In this paper, we explore how the two reasoning abilities can be jointly modeled to enhance both accuracy and explainability of machine learning models.
UP5: Unbiased Foundation Model for Fairness-aware Recommendation
However, at present, there is a lack of understanding regarding the level of fairness exhibited by recommendation foundation models and the appropriate methods for equitably treating different groups of users in foundation models.
How to Index Item IDs for Recommendation Foundation Models
To emphasize the importance of item indexing, we first discuss the issues of several trivial item indexing methods, such as random indexing, title indexing, and independent indexing.
Automated Data Denoising for Recommendation
In real-world scenarios, most platforms collect both large-scale, naturally noisy implicit feedback and small-scale yet highly relevant explicit feedback.
OpenAGI: When LLM Meets Domain Experts
This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents, enabling them to harness expert models for complex task-solving towards Artificial General Intelligence (AGI).
Fairness-aware Differentially Private Collaborative Filtering
Recently, there has been an increasing adoption of differential privacy guided algorithms for privacy-preserving machine learning tasks.
Causal Inference for Recommendation: Foundations, Methods and Applications
We discuss the fundamental concepts of both recommender systems and causal inference as well as their relationship, and review the existing work on causal methods for different problems in recommender systems.
A Survey on Trustworthy Recommender Systems
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process.
Fairness in Recommendation: Foundations, Methods and Applications
It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking in order to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems.
AutoLossGen: Automatic Loss Function Generation for Recommender Systems
One challenge for automatic loss generation in recommender systems is the extreme sparsity of recommendation datasets, which leads to the sparse reward problem for loss generation and search.
Explainable Fairness in Recommendation
In this paper, we study the problem of explainable fairness, which helps to gain insights about why a system is fair or unfair, and guides the design of fair recommender systems with a more informed and unified methodology.
Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)
For a long time, different recommendation tasks typically require designing task-specific architectures and training objectives.
Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning
For quantitatively evaluating the generated explanations without the requirement of ground-truth, we design metrics based on Counterfactual and Factual reasoning to evaluate the necessity and sufficiency of the explanations.
Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning
One conspicuous approach is to seek a Pareto efficient solution to guarantee optimal compromises between utility and fairness.
Counterfactual Evaluation for Explainable AI
While recent years have witnessed the emergence of various explainable methods in machine learning, to what degree the explanations really represent the reasoning process behind the model prediction -- namely, the faithfulness of explanation -- is still an open problem.
Counterfactual Explainable Recommendation
Technically, for each item recommended to each user, CountER formulates a joint optimization problem to generate minimal changes on the item aspects so as to create a counterfactual item, such that the recommendation decision on the counterfactual item is reversed.
FAIR: Fairness-Aware Information Retrieval Evaluation
We believe our work opens up a new direction of pursuing a metric for evaluating and implementing the FAIR systems.
Personalized Counterfactual Fairness in Recommendation
Therefore, it is important to provide personalized fair recommendations for users to satisfy their personalized fairness demands.
Efficient Non-Sampling Knowledge Graph Embedding
Experiments on benchmark datasets show that our NS-KGE framework can achieve a better performance on efficiency and accuracy over traditional negative sampling based models, and that the framework is applicable to a large class of knowledge graph embedding models.
User-oriented Fairness in Recommendation
To solve this problem, we provide a re-ranking approach to mitigate this unfairness problem by adding constraints over evaluation metrics.
Variation Control and Evaluation for Generative SlateRecommendations
Slate recommendation generates a list of items as a whole instead of ranking each item individually, so as to better model the intra-list positional biases and item relations.
Causal Collaborative Filtering
However, pure correlative learning may lead to Simpson's paradox in predictions, and thus results in sacrificed recommendation performance.
Towards Long-term Fairness in Recommendation
We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process.
CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
User profiles can capture prominent user behaviors from the history, and provide valuable signals about which kinds of path patterns are more likely to lead to potential items of interest for the user.
Learning Personalized Risk Preferences for Recommendation
Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and review information to make purchase decisions.
Understanding Echo Chambers in E-commerce Recommender Systems
Current research on recommender systems mostly focuses on matching users with proper items based on user interests.
Fairness-Aware Explainable Recommendation over Knowledge Graphs
There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems.
ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs
A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal.
