56 research outputs found
ExplainableFold: Understanding AlphaFold Prediction with Explainable AI
This paper presents ExplainableFold, an explainable AI framework for protein
structure prediction. Despite the success of AI-based methods such as AlphaFold
in this field, the underlying reasons for their predictions remain unclear due
to the black-box nature of deep learning models. To address this, we propose a
counterfactual learning framework inspired by biological principles to generate
counterfactual explanations for protein structure prediction, enabling a
dry-lab experimentation approach. Our experimental results demonstrate the
ability of ExplainableFold to generate high-quality explanations for
AlphaFold's predictions, providing near-experimental understanding of the
effects of amino acids on 3D protein structure. This framework has the
potential to facilitate a deeper understanding of protein structures.Comment: This work has been accepted for presentation at the 29th ACM SIGKDD
Conference on Knowledge Discovery and Data Mining (KDD 2023
Deconfounded Causal Collaborative Filtering
Recommender systems may be confounded by various types of confounding factors
(also called confounders) that may lead to inaccurate recommendations and
sacrificed recommendation performance. Current approaches to solving the
problem usually design each specific model for each specific confounder.
However, real-world systems may include a huge number of confounders and thus
designing each specific model for each specific confounder is unrealistic. More
importantly, except for those "explicit confounders" that researchers can
manually identify and process such as item's position in the ranking list,
there are also many "latent confounders" that are beyond the imagination of
researchers. For example, users' rating on a song may depend on their current
mood or the current weather, and users' preference on ice creams may depend on
the air temperature. Such latent confounders may be unobservable in the
recorded training data. To solve the problem, we propose a deconfounded causal
collaborative filtering model. We first frame user behaviors with unobserved
confounders into a causal graph, and then we design a front-door adjustment
model carefully fused with machine learning to deconfound the influence of
unobserved confounders. The proposed model is able to handle both global
confounders and personalized confounders. Experiments on real-world e-commerce
datasets show that our method is able to deconfound unobserved confounders to
achieve better recommendation performance.Comment: 9 pages, 5 figures; comments and suggestions are highly appreciate
VIP5: Towards Multimodal Foundation Models for Recommendation
Computer Vision (CV), Natural Language Processing (NLP), and Recommender
Systems (RecSys) are three prominent AI applications that have traditionally
developed independently, resulting in disparate modeling and engineering
methodologies. This has impeded the ability for these fields to directly
benefit from each other's advancements. With the recent development of
foundation models, large language models have emerged as a potential
general-purpose interface for unifying different modalities and problem
formulations. In light of this, we propose the development of a multimodal
foundation model (MFM) considering visual, textual, and personalization
modalities under the P5 recommendation paradigm, thus named VIP5 (Visual P5),
to unify various modalities and recommendation tasks. This will enable the
processing of multiple modalities in a shared architecture for improved
recommendations. To achieve this, we introduce multimodal personalized prompts
to accommodate multiple modalities under a shared format. Additionally, we
propose a parameter-efficient training method for foundation models, which
involves freezing the P5 backbone and fine-tuning lightweight adapters,
resulting in improved recommendation performance and increased efficiency in
terms of training time and memory usage. Code and data of VIP5 are available at
https://github.com/jeykigung/VIP5.Comment: Accepted by EMNLP 202
OpenAGI: When LLM Meets Domain Experts
Human intelligence has the remarkable ability to assemble basic skills into
complex ones so as to solve complex tasks. This ability is equally important
for Artificial Intelligence (AI), and thus, we assert that in addition to the
development of large, comprehensive intelligent models, it is equally crucial
to equip such models with the capability to harness various domain-specific
expert models for complex task-solving in the pursuit of Artificial General
Intelligence (AGI). Recent developments in Large Language Models (LLMs) have
demonstrated remarkable learning and reasoning abilities, making them promising
as a controller to select, synthesize, and execute external models to solve
complex tasks. In this project, we develop OpenAGI, an open-source AGI research
platform, specifically designed to offer complex, multi-step tasks and
accompanied by task-specific datasets, evaluation metrics, and a diverse range
of extensible models. OpenAGI formulates complex tasks as natural language
queries, serving as input to the LLM. The LLM subsequently selects,
synthesizes, and executes models provided by OpenAGI to address the task.
Furthermore, we propose a Reinforcement Learning from Task Feedback (RLTF)
mechanism, which uses the task-solving result as feedback to improve the LLM's
task-solving ability. Thus, the LLM is responsible for synthesizing various
external models for solving complex tasks, while RLTF provides feedback to
improve its task-solving ability, enabling a feedback loop for self-improving
AI. We believe that the paradigm of LLMs operating various expert models for
complex task-solving is a promising approach towards AGI. To facilitate the
community's long-term improvement and evaluation of AGI's ability, we
open-source the code, benchmark, and evaluation methods of the OpenAGI project
at https://github.com/agiresearch/OpenAGI.Comment: 18 pages, 6 figures, 7 table
GenRec: Large Language Model for Generative Recommendation
In recent years, large language models (LLM) have emerged as powerful tools
for diverse natural language processing tasks. However, their potential for
recommender systems under the generative recommendation paradigm remains
relatively unexplored. This paper presents an innovative approach to
recommendation systems using large language models (LLMs) based on text data.
In this paper, we present a novel LLM for generative recommendation (GenRec)
that utilized the expressive power of LLM to directly generate the target item
to recommend, rather than calculating ranking score for each candidate item one
by one as in traditional discriminative recommendation. GenRec uses LLM's
understanding ability to interpret context, learn user preferences, and
generate relevant recommendation. Our proposed approach leverages the vast
knowledge encoded in large language models to accomplish recommendation tasks.
We first we formulate specialized prompts to enhance the ability of LLM to
comprehend recommendation tasks. 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. Our research underscores the potential of LLM-based generative
recommendation in revolutionizing the domain of recommendation systems and
offers a foundational framework for future explorations in this field. We
conduct extensive experiments on benchmark datasets, and the experiments shows
that our GenRec has significant better results on large dataset
Fairness in Recommendation: Foundations, Methods and Applications
As one of the most pervasive applications of machine learning, recommender
systems are playing an important role on assisting human decision making. The
satisfaction of users and the interests of platforms are closely related to the
quality of the generated recommendation results. However, as a highly
data-driven system, recommender system could be affected by data or algorithmic
bias and thus generate unfair results, which could weaken the reliance of the
systems. As a result, it is crucial to address the potential unfairness
problems in recommendation settings. Recently, there has been growing attention
on fairness considerations in recommender systems with more and more literature
on approaches to promote fairness in recommendation. However, the studies are
rather fragmented and lack a systematic organization, thus making it difficult
to penetrate for new researchers to the domain. This motivates us to provide a
systematic survey of existing works on fairness in recommendation. This survey
focuses on the foundations for fairness in recommendation literature. 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. After that, the survey will introduce fairness in recommendation with
a focus on the taxonomies of current fairness definitions, the typical
techniques for improving fairness, as well as the datasets for fairness studies
in recommendation. The survey also talks about the challenges and opportunities
in fairness research with the hope of promoting the fair recommendation
research area and beyond.Comment: Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST
User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations
Modern recommender systems utilize users' historical behaviors to generate
personalized recommendations. However, these systems often lack user
controllability, leading to diminished user satisfaction and trust in the
systems. Acknowledging the recent advancements in explainable recommender
systems that enhance users' understanding of recommendation mechanisms, we
propose leveraging these advancements to improve user controllability. In this
paper, we present a user-controllable recommender system that seamlessly
integrates explainability and controllability within a unified framework. By
providing both retrospective and prospective explanations through
counterfactual reasoning, users can customize their control over the system by
interacting with these explanations.
Furthermore, we introduce and assess two attributes of controllability in
recommendation systems: the complexity of controllability and the accuracy of
controllability. Experimental evaluations on MovieLens and Yelp datasets
substantiate the effectiveness of our proposed framework. Additionally, our
experiments demonstrate that offering users control options can potentially
enhance recommendation accuracy in the future. Source code and data are
available at \url{https://github.com/chrisjtan/ucr}.Comment: Accepted for presentation at 26th European Conference on Artificial
Intelligence (ECAI2023
Counterfactual Collaborative Reasoning
Causal reasoning and logical reasoning are two important types of reasoning
abilities for human intelligence. However, their relationship has not been
extensively explored under machine intelligence context. In this paper, we
explore how the two reasoning abilities can be jointly modeled to enhance both
accuracy and explainability of machine learning models. More specifically, by
integrating two important types of reasoning ability -- counterfactual
reasoning and (neural) logical reasoning -- we propose Counterfactual
Collaborative Reasoning (CCR), which conducts counterfactual logic reasoning to
improve the performance. In particular, we use recommender system as an example
to show how CCR alleviate data scarcity, improve accuracy and enhance
transparency. Technically, we leverage counterfactual reasoning to generate
"difficult" counterfactual training examples for data augmentation, which --
together with the original training examples -- can enhance the model
performance. Since the augmented data is model irrelevant, they can be used to
enhance any model, enabling the wide applicability of the technique. Besides,
most of the existing data augmentation methods focus on "implicit data
augmentation" over users' implicit feedback, while our framework conducts
"explicit data augmentation" over users explicit feedback based on
counterfactual logic reasoning. Experiments on three real-world datasets show
that CCR achieves better performance than non-augmented models and implicitly
augmented models, and also improves model transparency by generating
counterfactual explanations
Predictive model for diabetic retinopathy under limited medical resources: A multicenter diagnostic study
BackgroundComprehensive eye examinations for diabetic retinopathy is poorly implemented in medically underserved areas. There is a critical need for a widely available and economical tool to aid patient selection for priority retinal screening. We investigated the possibility of a predictive model for retinopathy identification using simple parameters.MethodsClinical data were retrospectively collected from 4, 159 patients with diabetes admitted to five tertiary hospitals. Independent predictors were identified by univariate analysis and least absolute shrinkage and selection operator (LASSO) regression, and a nomogram was developed based on a multivariate logistic regression model. The validity and clinical practicality of this nomogram were assessed using concordance index (C-index), area under the receiver operating characteristic curve (AUROC), calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).ResultsThe predictive factors in the multivariate model included the duration of diabetes, history of hypertension, and cardiovascular disease. The three-variable model displayed medium prediction ability with an AUROC of 0.722 (95%CI 0.696-0.748) in the training set, 0.715 (95%CI 0.670-0.754) in the internal set, and 0.703 (95%CI 0.552-0.853) in the external dataset. DCA showed that the threshold probability of DR in diabetic patients was 17-55% according to the nomogram, and CIC also showed that the nomogram could be applied clinically if the risk threshold exceeded 30%. An operation interface on a webpage (https://cqmuxss.shinyapps.io/dr_tjj/) was built to improve the clinical utility of the nomogram.ConclusionsThe predictive model developed based on a minimal amount of clinical data available to diabetic patients with restricted medical resources could help primary healthcare practitioners promptly identify potential retinopathy
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