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Boi Faltings

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Related publications (160)

Page 4 of 8

Courtesy As a Means to Coordinate

Boi Faltings, Panayiotis Danassis

We investigate the problem of multi-agent coordination under rationality constraints. Specifically, role allocation, task assignment, resource allocation, etc. Inspired by human behavior, we propose a framework (CA^3NONY) that enables fast convergence to e ...

International Foundation for Autonomous Agents and Multiagent Systems2019

Multi-Step Critiquing User Interface for Recommender Systems

Boi Faltings, Diego Matteo Antognini, Diana Andreea Petrescu

Recommendations with personalized explanations have been shown to increase user trust and perceived quality and help users make better decisions. Moreover, such explanations allow users to provide feedback by critiquing them. Several algorithms for recomme ...

ASSOC COMPUTING MACHINERY2021

DUCT: An upper confidence bound approach to distributed constraint optimization problems

Boi Faltings, Christos Dimitrakakis, Brammert Ottens

The Upper Confidence Bounds (UCB) algorithm is a well-known near-optimal strategy for the stochastic multi-armed bandit problem. Its extensions to trees, such as the Upper Confidence Tree (UCT) algorithm, have resulted in good solutions to the problem of G ...

2012

Differentially Private Multi-Agent Constraint Optimization

Boi Faltings, Sujit Prakash Gujar, Aleksei Triastcyn, Sankarshan Damle

Several optimization scenarios involve multiple agents that desire to protect the privacy of their preferences. There are distributed algorithms for constraint optimization that provide improved privacy protection through secure multiparty computation. How ...

ACM2022

Momentum-based Gradient Methods in Multi-Objective Recommendation

Boi Faltings, Diego Matteo Antognini, Claudiu-Cristian Musat

Multi-objective gradient methods are becoming the standard for solving multi-objective problems. Among others, they show promising results in developing multi-objective recommender systems with both correlated and conflicting objectives. Classic multi-grad ...

2021

Non-Discriminatory Machine Learning through Convex Fairness Criteria

Boi Faltings, Naman Goel

Biased decision making by machine learning systems is increasingly recognized as an important issue. Recently, techniques have been proposed to learn non-discriminatory classifiers by enforcing constraints in the training phase. Such constraints are either ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2018

PEN recsys: a Personalized News Recommender Systems Framework (DEMO)

Boi Faltings, Florent Frédéric Garcin

We present the Personalized News (PEN) recommender systems framework, (Note that this paper is an extended abstract of Garcin2013b), currently in use by a newspaper website to evaluate various algorithms for news recommendations. We briefly describe its sy ...

2013

A Truthful, Privacy-Preserving, Approximately Efficient Combinatorial Auction For Single-minded Bidders

Boi Faltings, Sujit Prakash Gujar, Sankarshan Damle

Combinatorial auctions are widely used to sell resources/items. The challenges in such auctions are multi-fold. We need to ensure that bidders, the strategic agents, bid their valuations truthfully to the auction mechanism. Besides, the agents may desire p ...

ASSOC COMPUTING MACHINERY2019

On the Parallels Between Evolutionary Theory and the State of AI

Boi Faltings, Zeki Doruk Erden

This article critically examines the foundational principles of contemporary AI methods, exploring the limitations that hinder its potential. We draw parallels between the modern AI landscape and the 20th-century Modern Synthesis in evolutionary biology, a ...

ACM2025

Personalized news recommendation based on collaborative filtering

Boi Faltings, Vincent Jean Fabrice Schickel, Florent Frédéric Garcin

Because of the abundance of news on the web, news recommendation is an important problem. We compare three approaches for personalized news recommendation: collaborative filtering at the level of news items, content-based system recommending items with sim ...

2012

Agile preference models based on soft constraints

Boi Faltings, Pearl Pu Faltings, Jiyong Zhang

An accurate model of the user's preferences is a crucial element of most decision support systems. It is often assumed that users have a well-defined and stable set of preferences that can be elicited through a set of questions. However, recent research ha ...

American Association for Artificial Intelligence, Menlo Park, CA 94025-3496, United States2005

Recommendation using textual opinions

Boi Faltings, Claudiu-Cristian Musat

Many web sites collect reviews of products and services and use them provide rankings of their quality. However, such rankings are not personalized. We investigate how the information in the reviews written by a particular user can be used to personalize t ...

2013

Improving Multi-agent Coordination by Learning to Estimate Contention

Boi Faltings, Panayiotis Danassis

We present a multi-agent learning algorithm, ALMA-Learning, for efficient and fair allocations in large-scale systems. We circumvent the traditional pitfalls of multi-agent learning (e.g., the moving target problem, the curse of dimensionality, or the need ...

2021

A Model of Online Social Interactions based on Sentiment Analysis and Content Similarity

Boi Faltings, Claudiu-Cristian Musat

In this paper we create a model of human behavior in online communities, based on the network topology and on the communication content. The model contains eleven distinct hypotheses, which validate three intuitions. The rst intuition is that the network t ...

2013

Using Hierarchical Clustering for Learning the Ontologies used in Recommendation Systems

Boi Faltings, Vincent Jean Fabrice Schickel

Ontologies are being successfully used to overcome semantic heterogeneity, and are becoming fundamental elements of the Semantic Web. Recently, it has also been shown that ontologies can be used to build more accurate and more personalized recommendation s ...

2007

Overcoming Incomplete User Models in Recommendation Systems Via an Ontology

Boi Faltings, Vincent Jean Fabrice Schickel

To make accurate recommendations, recommendation systems currently require more data about a customer than is usually available. We conjecture that the weaknesses are due to a lack of inductive bias in the learning methods used to build the prediction mode ...

Springer2006

OpenSense: open community driven sensing of environment

Karl Aberer, Boi Faltings, Alcherio Martinoli, Guillermo Barrenetxea Kobas, Dipanjan Chakraborty, Saket Sathe

This paper outlines a vision for community-driven sensing of our environment. At its core, community sensing is a dynamic new form of mobile geosensor network. We believe that community sensing networks, in order to be widely deployable and sustainable, ne ...

ACM2010

REFINER: Reasoning Feedback on Intermediate Representations

Boi Faltings, Robert West, Antoine Bosselut, Maxime Jean Julien Peyrard, Debjit Paul, Mahammad Ismayilzada, Beatriz Maria Borges Ribeiro

Language models (LMs) have recently shown remarkable performance on reasoning tasks by explicitly generating intermediate inferences, e.g., chain-of-thought prompting. However, these intermediate inference steps may be in- appropriate deductions from the i ...

2023

Personalizing Product Rankings Using Collaborative Filtering on Opinion-Derived Topic Profiles

Boi Faltings, Claudiu-Cristian Musat

Product review sites such as TripAdvisor, Yelp or Amazon provide a single, non personalized ranking of products. The sparse review data makes personalizing recommendations difficult. Topic Profile Collaborative Filtering exploits review texts to identify u ...

2015

Distributed Constraint Optimization with Structured Resource Constraints

Boi Faltings, Adrian Petcu

Distributed constraint optimization (DCOP) provides a framework for coordinated decision making by a team of agents. Often, during the decision making, capacity constraints on agents' resource consumption must be taken into account. To address such scenari ...

2009