264 research outputs found
Leveraging Large Language Models in Conversational Recommender Systems
A Conversational Recommender System (CRS) offers increased transparency and
control to users by enabling them to engage with the system through a real-time
multi-turn dialogue. Recently, Large Language Models (LLMs) have exhibited an
unprecedented ability to converse naturally and incorporate world knowledge and
common-sense reasoning into language understanding, unlocking the potential of
this paradigm. However, effectively leveraging LLMs within a CRS introduces new
technical challenges, including properly understanding and controlling a
complex conversation and retrieving from external sources of information. These
issues are exacerbated by a large, evolving item corpus and a lack of
conversational data for training. In this paper, we provide a roadmap for
building an end-to-end large-scale CRS using LLMs. In particular, we propose
new implementations for user preference understanding, flexible dialogue
management and explainable recommendations as part of an integrated
architecture powered by LLMs. For improved personalization, we describe how an
LLM can consume interpretable natural language user profiles and use them to
modulate session-level context. To overcome conversational data limitations in
the absence of an existing production CRS, we propose techniques for building a
controllable LLM-based user simulator to generate synthetic conversations. As a
proof of concept we introduce RecLLM, a large-scale CRS for YouTube videos
built on LaMDA, and demonstrate its fluency and diverse functionality through
some illustrative example conversations
Large Language Models for Generative Recommendation: A Survey and Visionary Discussions
Recent years have witnessed the wide adoption of large language models (LLM)
in different fields, especially natural language processing and computer
vision. Such a trend can also be observed in recommender systems (RS). However,
most of related work treat LLM as a component of the conventional
recommendation pipeline (e.g., as a feature extractor) which may not be able to
fully leverage the generative power of LLM. Instead of separating the
recommendation process into multiple stages such as score computation and
re-ranking, this process can be simplified to one stage with LLM: directly
generating recommendations from the complete pool of items. This survey reviews
the progress, methods and future directions of LLM-based generative
recommendation by examining three questions: 1) What generative recommendation
is, 2) Why RS should advance to generative recommendation, and 3) How to
implement LLM-based generative recommendation for various RS tasks. We hope
that the survey can provide the context and guidance needed to explore this
interesting and emerging topic
A personality aware recommendation system
Les systĂšmes de recommandation conversationnels (CRSs) sont des systĂšmes qui fournissent
des recommandations personnalisĂ©es par le biais dâune session de dialogue en langage
naturel avec les utilisateurs. Contrairement aux systĂšmes de recommandation traditionnels
qui ne prennent comme vérité de base que les préférences anciennes des utilisateurs, les
CRS impliquent aussi les préférences actuelles des utilisateurs durant la conversation. Des
recherches récentes montrent que la compréhension de la signification contextuelle des
préférences des utilisateurs et des dialogues peut améliorer de maniÚre significative les
performances du systÚme de recommandation. Des chercheurs ont également montré un
lien fort entre les traits de personnalité des utilisateurs et les systÚmes de recommandation.
La personnalité et les préférences sont des variables essentielles en sciences sociales. Elles
décrivent les différences entre les personnes, que ce soit au niveau individuel ou collectif.
Les approches récentes de recommandation basées sur la personnalité sont des systÚmes non
conversationnels. Par consĂ©quent, il est extrĂȘmement important de dĂ©tecter et dâutiliser les
traits de personnalitĂ© des individus dans les systĂšmes conversationnels afin dâassurer une
performance de recommandation et de dialogue plus personnalisée. Pour ce faire, ce travail
propose un systÚme de recommandation conversationnel sensible à la personnalité qui est
basé sur des modules qui assurent une session de dialogue et recommandation personnalisée
en utilisant les traits de personnalité des utilisateurs. Nous proposons également une
nouvelle approche de détection de la personnalité, qui est un modÚle de langage spécifique
au contexte pour détecter les traits des individus en utilisant leurs données publiées sur les
réseaux sociaux. Les résultats montrent que notre systÚme proposé a surpassé les approches
existantes dans différentes mesures.A Conversational Recommendation System (CRS) is a system that provides personalized
recommendations through a session of natural language dialogue turns with users. Unlike
traditional one-shot recommendation systems, which only assume the userâs previous
preferences as the ground truth, CRS uses both previous and current user preferences.
Recent research shows that understanding the contextual meaning of user preferences and
dialogue turns can significantly improve recommendation performance. It also shows a
strong link between usersâ personality traits and recommendation systems. Personality
and preferences are essential variables in computational sociology and social science.
They describe the differences between people, both at the individual and collective level.
Recent personality-based recommendation approaches are traditional one-shot systems, or
ânon conversational systemsâ. Therefore, there is a significant need to detect and employ
individualsâ personality traits within the CRS paradigm to ensure a better and more
personalized dialogue recommendation performance.
Driven by the aforementioned facts, this study proposes a modularized, personality-
aware CRS that ensures a personalized dialogue recommendation session using the usersâ
personality traits. We also propose a novel personality detection approach, which is a
context-specific language model for detecting individualsâ personality traits using their
social media data. The goal is to create a personality-aware and topic-guided CRS model
that performs better than the standard CRS models. Experimental results show that our
personality-aware conversation recommendation system has outperformed state-of-the-art
approaches in different considered metrics on the topic-guided conversation recommendation
dataset
Computational Technologies for Fashion Recommendation: A Survey
Fashion recommendation is a key research field in computational fashion
research and has attracted considerable interest in the computer vision,
multimedia, and information retrieval communities in recent years. Due to the
great demand for applications, various fashion recommendation tasks, such as
personalized fashion product recommendation, complementary (mix-and-match)
recommendation, and outfit recommendation, have been posed and explored in the
literature. The continuing research attention and advances impel us to look
back and in-depth into the field for a better understanding. In this paper, we
comprehensively review recent research efforts on fashion recommendation from a
technological perspective. We first introduce fashion recommendation at a macro
level and analyse its characteristics and differences with general
recommendation tasks. We then clearly categorize different fashion
recommendation efforts into several sub-tasks and focus on each sub-task in
terms of its problem formulation, research focus, state-of-the-art methods, and
limitations. We also summarize the datasets proposed in the literature for use
in fashion recommendation studies to give readers a brief illustration.
Finally, we discuss several promising directions for future research in this
field. Overall, this survey systematically reviews the development of fashion
recommendation research. It also discusses the current limitations and gaps
between academic research and the real needs of the fashion industry. In the
process, we offer a deep insight into how the fashion industry could benefit
from fashion recommendation technologies. the computational technologies of
fashion recommendation
Enhancing explainability and scrutability of recommender systems
Our increasing reliance on complex algorithms for recommendations calls for models and methods for explainable, scrutable, and trustworthy AI. While explainability is required for understanding the relationships between model inputs and outputs, a scrutable system allows us to modify its behavior as desired. These properties help bridge the gap between our expectations and the algorithmâs behavior and accordingly boost our trust in AI. Aiming to cope with information overload, recommender systems play a crucial role in ïŹltering content (such as products, news, songs, and movies) and shaping a personalized experience for their users. Consequently, there has been a growing demand from the information consumers to receive proper explanations for their personalized recommendations. These explanations aim at helping users understand why certain items are recommended to them and how their previous inputs to the system relate to the generation of such recommendations. Besides, in the event of receiving undesirable content, explanations could possibly contain valuable information as to how the systemâs behavior can be modiïŹed accordingly. In this thesis, we present our contributions towards explainability and scrutability of recommender systems: âą We introduce a user-centric framework, FAIRY, for discovering and ranking post-hoc explanations for the social feeds generated by black-box platforms. These explanations reveal relationships between usersâ proïŹles and their feed items and are extracted from the local interaction graphs of users. FAIRY employs a learning-to-rank (LTR) method to score candidate explanations based on their relevance and surprisal. âą We propose a method, PRINCE, to facilitate provider-side explainability in graph-based recommender systems that use personalized PageRank at their core. PRINCE explanations are comprehensible for users, because they present subsets of the userâs prior actions responsible for the received recommendations. PRINCE operates in a counterfactual setup and builds on a polynomial-time algorithm for ïŹnding the smallest counterfactual explanations. âą We propose a human-in-the-loop framework, ELIXIR, for enhancing scrutability and subsequently the recommendation models by leveraging user feedback on explanations. ELIXIR enables recommender systems to collect user feedback on pairs of recommendations and explanations. The feedback is incorporated into the model by imposing a soft constraint for learning user-speciïŹc item representations. We evaluate all proposed models and methods with real user studies and demonstrate their beneïŹts at achieving explainability and scrutability in recommender systems.Unsere zunehmende AbhĂ€ngigkeit von komplexen Algorithmen fĂŒr maschinelle Empfehlungen erfordert Modelle und Methoden fĂŒr erklĂ€rbare, nachvollziehbare und vertrauenswĂŒrdige KI. Zum Verstehen der Beziehungen zwischen Modellein- und ausgaben muss KI erklĂ€rbar sein. Möchten wir das Verhalten des Systems hingegen nach unseren Vorstellungen Ă€ndern, muss dessen Entscheidungsprozess nachvollziehbar sein. ErklĂ€rbarkeit und Nachvollziehbarkeit von KI helfen uns dabei, die LĂŒcke zwischen dem von uns erwarteten und dem tatsĂ€chlichen Verhalten der Algorithmen zu schlieĂen und unser Vertrauen in KI-Systeme entsprechend zu stĂ€rken. Um ein ĂbermaĂ an Informationen zu verhindern, spielen Empfehlungsdienste eine entscheidende Rolle um Inhalte (z.B. Produkten, Nachrichten, Musik und Filmen) zu ïŹltern und deren Benutzern eine personalisierte Erfahrung zu bieten. Infolgedessen erheben immer mehr In- formationskonsumenten Anspruch auf angemessene ErklĂ€rungen fĂŒr deren personalisierte Empfehlungen. Diese ErklĂ€rungen sollen den Benutzern helfen zu verstehen, warum ihnen bestimmte Dinge empfohlen wurden und wie sich ihre frĂŒheren Eingaben in das System auf die Generierung solcher Empfehlungen auswirken. AuĂerdem können ErklĂ€rungen fĂŒr den Fall, dass unerwĂŒnschte Inhalte empfohlen werden, wertvolle Informationen darĂŒber enthalten, wie das Verhalten des Systems entsprechend geĂ€ndert werden kann. In dieser Dissertation stellen wir unsere BeitrĂ€ge zu ErklĂ€rbarkeit und Nachvollziehbarkeit von Empfehlungsdiensten vor. âą Mit FAIRY stellen wir ein benutzerzentriertes Framework vor, mit dem post-hoc ErklĂ€rungen fĂŒr die von Black-Box-Plattformen generierten sozialen Feeds entdeckt und bewertet werden können. Diese ErklĂ€rungen zeigen Beziehungen zwischen BenutzerproïŹlen und deren Feeds auf und werden aus den lokalen Interaktionsgraphen der Benutzer extrahiert. FAIRY verwendet eine LTR-Methode (Learning-to-Rank), um die ErklĂ€rungen anhand ihrer Relevanz und ihres Grads unerwarteter Empfehlungen zu bewerten. âą Mit der PRINCE-Methode erleichtern wir das anbieterseitige Generieren von ErklĂ€rungen fĂŒr PageRank-basierte Empfehlungsdienste. PRINCE-ErklĂ€rungen sind fĂŒr Benutzer verstĂ€ndlich, da sie Teilmengen frĂŒherer Nutzerinteraktionen darstellen, die fĂŒr die erhaltenen Empfehlungen verantwortlich sind. PRINCE-ErklĂ€rungen sind somit kausaler Natur und werden von einem Algorithmus mit polynomieller Laufzeit erzeugt , um prĂ€zise ErklĂ€rungen zu ïŹnden. âą Wir prĂ€sentieren ein Human-in-the-Loop-Framework, ELIXIR, um die Nachvollziehbarkeit der Empfehlungsmodelle und die QualitĂ€t der Empfehlungen zu verbessern. Mit ELIXIR können Empfehlungsdienste Benutzerfeedback zu Empfehlungen und ErklĂ€rungen sammeln. Das Feedback wird in das Modell einbezogen, indem benutzerspeziïŹscher Einbettungen von Objekten gelernt werden. Wir evaluieren alle Modelle und Methoden in Benutzerstudien und demonstrieren ihren Nutzen hinsichtlich ErklĂ€rbarkeit und Nachvollziehbarkeit von Empfehlungsdiensten
Automatic management tool for attribution and monitorization of projects/internships
No Ășltimo ano acadĂ©mico, os estudantes do ISEP necessitam de realizar um projeto final para
obtenção do grau académico que pretendem alcançar. O ISEP fornece uma plataforma digital
onde Ă© possĂvel visualizar todos os projetos que os alunos se podem candidatar. Apesar das
vantagens que a plataforma digital traz, esta também possui alguns problemas, nomeadamente
a difĂcil escolha de projetos adequados ao estudante devido Ă excessiva oferta e falta de
mecanismos de filtragem. Para além disso, existe também uma indecisão acrescida para
selecionar um supervisor que seja compatĂvel para o projeto selecionado.
Tendo o aluno escolhido o projeto e o supervisor, dĂĄ-se inĂcio Ă fase de monitorização do
mesmo, que possui também os seus problemas, como o uso de diversas ferramentas que
posteriormente levam a possĂveis problemas de comunicação e dificuldade em manter um
histórico de versÔes do trabalho desenvolvido.
De forma a responder aos problemas mencionados, realizou-se um estudo aprofundado dos
tópicos de sistemas de recomendação aplicados a Machine Learning e Learning Management
Systems. Para cada um desses grandes temas, foram analisados sistemas semelhantes capazes
de solucionar o problema proposto, tais como sistemas de recomendação desenvolvidos em
artigos cientĂficos, aplicaçÔes comerciais e ferramentas como o ChatGPT.
Através da anålise do estado da arte, concluiu-se que a solução para os problemas propostos
seria a criação de uma aplicação Web para alunos e supervisores, que juntasse as duas
temåticas analisadas. O sistema de recomendação desenvolvido possui filtragem colaborativa
com factorização de matrizes, e filtragem por conteĂșdo com semelhança de cossenos. As
tecnologias utilizadas no sistema centram-se em Python no back-end (com o uso de TensorFlow
e NumPy para funcionalidades de Machine Learning) e Svelte no front-end. O sistema foi
inspirado numa arquitetura em microsserviços em que cada serviço é representado pelo seu
prĂłprio contentor de Docker, e disponibilizado ao pĂșblico atravĂ©s de um domĂnio pĂșblico.
O sistema foi avaliado atravĂ©s de trĂȘs mĂ©tricas: performance, confiabilidade e usabilidade. Foi
utilizada a ferramenta Quantitative Evaluation Framework para definir dimensÔes, fatores e
requisitos(e respetivas pontuaçÔes). Os estudantes que testaram a solução avaliaram o sistema
de recomendação com um valor de aproximadamente 7 numa escala de 1 a 10, e os valores de
precision, recall, false positive rate e F-Measure foram avaliados em 0.51, 0.71, 0.23 e 0.59
respetivamente. Adicionalmente, ambos os grupos classificaram a aplicação como intuitiva e
de fåcil utilização, com resultados a rondar o 8 numa escala de 1 em 10.In the last academic year, students at ISEP need to complete a final project to obtain the
academic degree they aim to achieve. ISEP provides a digital platform where all the projects
that students can apply for can be viewed. Besides the advantages this platform has, it also
brings some problems, such as the difficult selection of projects suited for the student due to
the excessive offering and lack of filtering mechanisms. Additionally, there is also increased
difficulty in selecting a supervisor compatible with their project.
Once the student has chosen the project and the supervisor, the monitoring phase begins,
which also has its issues, such as using various tools that may lead to potential communication
problems and difficulty in maintaining a version history of the work done.
To address the mentioned problems, an in-depth study of recommendation systems applied to
Machine Learning and Learning Management Systems was conducted. For each of these
themes, similar systems that could solve the proposed problem were analysed, such as
recommendation systems developed in scientific papers, commercial applications, and tools
like ChatGPT.
Through the analysis of the state of the art, it was concluded that the solution to the proposed
problems would be the creation of a web application for students and supervisors that
combines the two analysed themes. The developed recommendation system uses collaborative
filtering with matrix factorization and content-based filtering with cosine similarity. The
technologies used in the system are centred around Python on the backend (with the use of
TensorFlow and NumPy for Machine Learning functionalities) and Svelte on the frontend. The
system was inspired by a microservices architecture, where each service is represented by its
own Docker container, and it was made available online through a public domain.
The system was evaluated through performance, reliability, and usability. The Quantitative
Evaluation Framework tool was used to define dimensions, factors, and requirements (and their
respective scores). The students who tested the solution rated the recommendation system
with a value of approximately 7 on a scale of 1 to 10, and the precision, recall, false positive
rate, and F-Measure values were evaluated at 0.51, 0.71, 0.23, and 0.59, respectively.
Additionally, both groups rated the application as intuitive and easy to use, with ratings around
8 on a scale of 1 to 10
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
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
Automated machine learning (AutoML) seeks to build ML models with minimal
human effort. While considerable research has been conducted in the area of
AutoML in general, aiming to take humans out of the loop when building
artificial intelligence (AI) applications, scant literature has focused on how
AutoML works well in open-environment scenarios such as the process of training
and updating large models, industrial supply chains or the industrial
metaverse, where people often face open-loop problems during the search
process: they must continuously collect data, update data and models, satisfy
the requirements of the development and deployment environment, support massive
devices, modify evaluation metrics, etc. Addressing the open-environment issue
with pure data-driven approaches requires considerable data, computing
resources, and effort from dedicated data engineers, making current AutoML
systems and platforms inefficient and computationally intractable.
Human-computer interaction is a practical and feasible way to tackle the
problem of open-environment AI. In this paper, we introduce OmniForce, a
human-centered AutoML (HAML) system that yields both human-assisted ML and
ML-assisted human techniques, to put an AutoML system into practice and build
adaptive AI in open-environment scenarios. Specifically, we present OmniForce
in terms of ML version management; pipeline-driven development and deployment
collaborations; a flexible search strategy framework; and widely provisioned
and crowdsourced application algorithms, including large models. Furthermore,
the (large) models constructed by OmniForce can be automatically turned into
remote services in a few minutes; this process is dubbed model as a service
(MaaS). Experimental results obtained in multiple search spaces and real-world
use cases demonstrate the efficacy and efficiency of OmniForce
DiQAD: A Benchmark Dataset for End-to-End Open-domain Dialogue Assessment
Dialogue assessment plays a critical role in the development of open-domain
dialogue systems. Existing work are uncapable of providing an end-to-end and
human-epistemic assessment dataset, while they only provide sub-metrics like
coherence or the dialogues are conversed between annotators far from real user
settings. In this paper, we release a large-scale dialogue quality assessment
dataset (DiQAD), for automatically assessing open-domain dialogue quality.
Specifically, we (1) establish the assessment criteria based on the dimensions
conforming to human judgements on dialogue qualities, and (2) annotate
large-scale dialogues that conversed between real users based on these
annotation criteria, which contains around 100,000 dialogues. We conduct
several experiments and report the performances of the baselines as the
benchmark on DiQAD. The dataset is openly accessible at
https://github.com/yukunZhao/Dataset_Dialogue_quality_evaluation.Comment: Accepted to Findings of EMNLP 202
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