8 research outputs found
Serialized Knowledge Enhanced Multi-objective Person-job Matching Recommendation in a High Mobility Job Market
In a high mobility job market, accumulated historical sequences information from persons and jobs bring opportunities and challenges to person-job matching recommendation, where the latent preferences may significantly determine the success of person-job matching. Moreover, the sparse labels further limit the learning performance of recommendation methods. To this end, we propose a novel serialized knowledge enhancement multi-objective person-job matching recommendation method, namely SMP-JM. The key idea is to design a serialized multi-objective method from “intention-delivery-review”, which effectively solves the problem of sparsity through the transmission of information and the serialization constraints between objectives. Specifically, we design various attention modules, such as self-attention, cross-attention and an orthogonal multi-head attention, to identify correlations between diversified features. Furthermore, a multi-granularity convolutional filtering module is design to extract personal latent preference from the historical sequential behaviors. Finally, the experimental results on a real-world dataset validate the performance of SMP-JM over the baseline methods
Preferences in Case-Based Reasoning
Case-based reasoning (CBR) is a well-established problem solving paradigm
that has been used in a wide range of real-world applications. Despite
its great practical success, work on the theoretical foundations of CBR is
still under way, and a coherent and universally applicable methodological
framework is yet missing. The absence of such a framework inspired the
motivation for the work developed in this thesis. Drawing on recent research
on preference handling in Artificial Intelligence and related fields, the goal of
this work is to develop a well theoretically-founded framework on the basis
of formal concepts and methods for knowledge representation and reasoning
with preferences
Linking Research and Policy: Assessing a Framework for Organic Agricultural Support in Ireland
This paper links social science research and agricultural policy through an analysis of support for organic agriculture and food. Globally, sales of organic food have experienced 20% annual increases for the past two decades, and represent the fastest growing segment of the grocery market. Although consumer interest has increased, farmers are not keeping up with demand. This is partly due to a lack of political support provided to farmers in their transition from conventional to organic production. Support policies vary by country and in some nations, such as the US, vary by state/province. There have been few attempts to document the types of support currently in place. This research draws on an existing Framework tool to investigate regionally specific and relevant policy support available to organic farmers in Ireland. This exploratory study develops a case study of Ireland within the framework of ten key categories of organic agricultural support: leadership, policy, research, technical support, financial support, marketing and promotion, education and information, consumer issues, inter-agency activities, and future developments. Data from the Irish Department of Agriculture, Fisheries and Food, the Irish Agriculture and Food Development Authority (Teagasc), and other governmental and semi-governmental agencies provide the basis for an assessment of support in each category. Assessments are based on the number of activities, availability of information to farmers, and attention from governmental personnel for each of the ten categories. This policy framework is a valuable tool for farmers, researchers, state agencies, and citizen groups seeking to document existing types of organic agricultural support and discover policy areas which deserve more attention
Raciocínio baseado em casos como recomendador de conteúdo pedagógico
Orientador : Prof. Dr. Fabiano SilvaDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 31/08/2016Inclui referências : f. 63-67Resumo: Raciocínio baseado em Casos (CBR) e um método para resolver problemas com recuperação de solucões para problemas anteriores. CBR demanda uma representacão de conhecimento que permite o resolvedor a buscar casos similareas atraves de uma pergunta e a taxa de similaridade e dado pela distancia em uma estrutura de arvore, uma ontologia. O objetivo da presente pesquisa e utilizar CBR como uma ferramenta pedagógica atraves de quatro pilares: Raciocínio Baseado em Casos, Representacao de conhecimento, Informatica Educacional e a mediacao do erro na educacao. Considera-se erro como uma questao de importancia no desenvolvimento pedagogico, entao isso deve ser mediado. A mediacao de erro e utilizada como um guia para uma classificaçao quantitativa de exercícios, levando em consideraçao quantas vezes um exercício foi respondido erroneamente, a distancia entre os exercícios da suas similaridades. Esse tipo de classificacao automatica para exercícios em sistemas de apoio educacional e uma das principais contribuições dessa pesquisa. Este trabalho sugere que o ciclo CBR e profícuo no desenvolvimento de uma ferramenta para criaçao automatica de exames. Palavras-chave: raciocínio baseado em casos, mediacao por erros, representacao do conhecimento, informatica educacional.Abstract: Case-based Reasoning (CBR) is a method for solving problems with similar retained solutions. CBR demands a knowledge representation that allows the reasoner to find similar cases by a query and the similarity rate is given by a distance in hierarchical tree structure, an ontology. The main goal of this research is to use CBR as a pedagogical tool supported by three pillars: Case-based reasoning, Knowledge representation and Error Mediation in Education. It is considered that the error has a role of importance in the pedagogical development, so it has to be mediated. The error mediation is used as a rule for a quantitative way of exercises classification, it takes into account how many times an exercise have been uncorrected answered, the distance between exercises gives the similarity between them. This kind of automatically classification for exercises in a educational support systems is one of the main contributions of this research. This work suggests that the CBR cycle is useful in the designing of a tool for automatic creation of exams. Keywords: case-based reasoning, error mediation, knowlodge representation, educational informatics
Prediction of user behaviour on the web
The Web has become an ubiquitous environment for human interaction, communication,
and data sharing. As a result, large amounts of data are produced. This
data can be utilised by building predictive models of user behaviour in order to support
business decisions. However, the fast pace of modern businesses is creating the
pressure on industry to provide faster and better decisions. This thesis addresses
this challenge by proposing a novel methodology for an effcient prediction of user
behaviour. The problems concerned are: (i) modelling user behaviour on the Web,
(ii) choosing and extracting features from data generated by user behaviour, and
(iii) choosing a Machine Learning (ML) set-up for an effcient prediction.
First, a novel Time-Varying Attributed Graph (TVAG) is introduced and
then a TVAG-based model for modelling user behaviour on the Web is proposed.
TVAGs capture temporal properties of user behaviour by their time varying component
of features of the graph nodes and edges. Second, the proposed model allows
to extract features for further ML predictions. However, extracting the features and
building the model may be unacceptably hard and long process. Thus, a guideline
for an effcient feature extraction from the TVAG-based model is proposed. Third,
a method for choosing a ML set-up to build an accurate and fast predictive model
is proposed and evaluated. Finally, a deep learning architecture for predicting user
behaviour on the Web is proposed and evaluated.
To sum up, the main contribution to knowledge of this work is in developing
the methodology for fast and effcient predictions of user behaviour on the Web.
The methodology is evaluated on datasets from a few Web platforms, namely Stack
Exchange, Twitter, and Facebook
Goal reasoning for autonomous agents using automated planning
Mención Internacional en el título de doctorAutomated planning deals with the task of finding a sequence of actions, namely
a plan, which achieves a goal from a given initial state. Most planning research
consider goals are provided by a external user, and agents just have to find a
plan to achieve them. However, there exist many real world domains where
agents should not only reason about their actions but also about their goals,
generating new ones or changing them according to the perceived environment.
In this thesis we aim at broadening the goal reasoning capabilities of planningbased
agents, both when acting in isolation and when operating in the same
environment as other agents.
In single-agent settings, we firstly explore a special type of planning tasks
where we aim at discovering states that fulfill certain cost-based requirements
with respect to a given set of goals. By computing these states, agents are able
to solve interesting tasks such as find escape plans that move agents in to safe
places, hide their true goal to a potential observer, or anticipate dynamically arriving
goals. We also show how learning the environment’s dynamics may help
agents to solve some of these tasks. Experimental results show that these states
can be quickly found in practice, making agents able to solve new planning
tasks and helping them in solving some existing ones.
In multi-agent settings, we study the automated generation of goals based on
other agents’ behavior. We focus on competitive scenarios, where we are interested
in computing counterplans that prevent opponents from achieving their
goals. We frame these tasks as counterplanning, providing theoretical properties
of the counterplans that solve them. We also show how agents can benefit
from computing some of the states we propose in the single-agent setting to
anticipate their opponent’s movements, thus increasing the odds of blocking
them. Experimental results show how counterplans can be found in different
environments ranging from competitive planning domains to real-time strategy
games.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidenta: Eva Onaindía de la Rivaherrera.- Secretario: Ángel García Olaya.- Vocal: Mark Robert
Active learning in recommender systems: an unbiased and beyond-accuracy perspective
The items that a Recommender System (RS) suggests to its users are typically ones that it thinks the user will like and want to consume. An RS that is good at its job is of interest not only to its customers but also to service providers, so they can secure long-term customers and increase revenue. Thus, there is a challenge in building better recommender systems.
One way to build a better RS is to improve the quality of the data on which the RS model is trained. An RS can use Active Learning (AL) to proactively acquire such data, with the goal of improving its model. The idea of AL for RS is to explicitly query the users, asking them to rate items which have not been rated yet. The items that a user will be asked to rate are known as the query items. Query items are different from recommendations. For example, the former may be items that the AL strategy predicts the user has already consumed, whereas the latter are ones that the RS predicts the user will like. In AL, query items are selected `intelligently' by an Active Learning strategy. Different AL strategies take different approaches to identify the query items.
As with the evaluation of RSs, preliminary evaluation of AL strategies must be done offline. An offline evaluation can help to narrow the number of promising strategies that need to be evaluated in subsequent costly user trials and online experiments. Where the literature describes the offline evaluation of AL, the evaluation is typically quite narrow and incomplete: mostly, the focus is cold-start users; the impact of newly-acquired ratings on recommendation quality is usually measured only for those users who supplied those ratings; and impact is measured in terms of prediction accuracy or recommendation relevance. Furthermore, the traditional AL evaluation does not take into account the bias problem. As brought to light by recent RS literature, this is a problem that affects the offline evaluation of RS; it arises when a biased dataset is used to perform the evaluation. We argue that it is a problem that affects offline evaluation of AL strategies too.
The main focus of this dissertation is on the design and evaluation of AL strategies for RSs. We first design novel methods (designated WTD and WTD_H) that `intervene' on a biased dataset to generate a new dataset with unbiased-like properties. Compared to the most similar approach proposed in the literature, we give empirical evidence, using two publicly-available datasets, that WTD and WTD_H are more effective at debiasing the evaluation of different recommender system models.
We then propose a new framework for offline evaluation of AL for RS, which we believe facilitates a more authentic picture of the performances of the AL strategies under evaluation. In particular, our framework uses WTD or WTD_H to mitigate the bias, but it also assesses the impact of AL in a more comprehensive way than the traditional evaluation used in the literature. Our framework is more comprehensive in at least two ways. First, it segments users in more ways than is conventional and analyses the impact of AL on the different segments. Second, in the same way that RS evaluation has changed from a narrow focus on prediction accuracy and recommendation relevance to a wider consideration of so-called `beyond-accuracy' criteria (such as diversity, serendipity and novelty), our framework extends the evaluation of AL strategies to also cover `beyond-accuracy' criteria. Experimental results on two datasets show the effectiveness of our new framework.
Finally, we propose some new AL strategies of our own. In particular, our new AL strategies, instead of focusing exclusively on prediction accuracy and recommendation relevance, are designed to also enhance `beyond-accuracy' criteria. We evaluate the new strategies using our more comprehensive evaluation framework