Preference relation-based markov random fields

A preference relation-based Top-N recommendation approach, PrefMRF, is proposed to capture both the second-order and the higher-order interactions among users and items. Traditionally Top-N recommendation was achieved by predicting the item ratings fi rst, and then inferring the item rankings, based on the assumption of availability of explicit feed-backs such as ratings, and the assumption that optimizing the ratings is equivalent to optimizing the item rankings. Nevertheless, both assumptions are not always true in real world applications. The proposed PrefMRF approach drops these assumptions by explicitly exploiting the preference relations, a more practical user feedback. Comparing to related work, the proposed PrefMRF approach has the unique property of modeling both the second-order and the higher-order interactions among users and items. To the best of our knowledge, this is the first time both types of interactions have been captured in preference relation-based method. Experiment results on public datasets demonstrate that both types of interactions have been properly captured, and signifi cantly improved Top-N recommendation performance has been achieved.

Preference relation-based Markov Random Fields for recommender systems

A preference relation-based Top-N recommendation approach is proposed to capture both second-order and higher-order interactions among users and items. Traditionally Top-N recommendation was achieved by predicting the item ratings first, and then inferring the item rankings, based on the assumption of availability of explicit feedback such as ratings, and the assumption that optimizing the ratings is equivalent to optimizing the item rankings. Nevertheless, both assumptions are not always true in real world applications. The proposed approach drops these assumptions by exploiting preference relations, a more practical user feedback. Furthermore, the proposed approach enjoys the representational power of Markov Random Fields thus side information such as item and user attributes can be easily incorporated. Comparing to related work, the proposed approach has the unique property of modeling both second-order and higher-order interactions among users and items. To the best of our knowledge, this is the first time both types of interactions have been captured in preference-relation based methods. Experimental results on public datasets demonstrate that both types of interactions have been properly captured, and significantly improved Top-N recommendation performance has been achieved.

Construction and aggregation of preference relations based on fuzzy partial orders

In group decision-making problems it is common to elicit preferences from human experts in the form of pairwise preference relations. When this is extended to a fuzzy setting, entries in the pairwise preference matrix are interpreted to denote strength of preference, however once logical properties such as consistency and transitivity are enforced, the resulting preference relation requires almost as much information as providing raw scores or a complete order over the alternatives. Here we instead interpret fuzzy degrees of preference to only apply where the preference over two alternatives is genuinely fuzzy and then suggest an aggregation procedure that minimizes a generalized Kemeny distance to the nearest complete or partial order. By focusing on the fuzzy partial order, the method is less affected by differences in the natural scale over which an expert expresses their preference, and can also limit the influence of extreme scores.

Relative preference-based recommender systems

This study investigates the problem of making recommendations to users, such as recommending a movie. Several novel models are proposed to make accurate recommendations by analyzing both the explicit and implicit data. Experiment results have confirmed improvements over state-of-the-art models.

Information enrichment for quality recommender systems

The explosive growth of the World-Wide-Web and the emergence of ecommerce are the major two factors that have led to the development of recommender systems (Resnick and Varian, 1997). The main task of recommender systems is to learn from users and recommend items (e.g. information, products or books) that match the users’ personal preferences. Recommender systems have been an active research area for more than a decade. Many different techniques and systems with distinct strengths have been developed to generate better quality recommendations. One of the main factors that affect recommenders’ recommendation quality is the amount of information resources that are available to the recommenders. The main feature of the recommender systems is their ability to make personalised recommendations for different individuals. However, for many ecommerce sites, it is difficult for them to obtain sufficient knowledge about their users. Hence, the recommendations they provided to their users are often poor and not personalised. This information insufficiency problem is commonly referred to as the cold-start problem. Most existing research on recommender systems focus on developing techniques to better utilise the available information resources to achieve better recommendation quality. However, while the amount of available data and information remains insufficient, these techniques can only provide limited improvements to the overall recommendation quality. In this thesis, a novel and intuitive approach towards improving recommendation quality and alleviating the cold-start problem is attempted. This approach is enriching the information resources. It can be easily observed that when there is sufficient information and knowledge base to support recommendation making, even the simplest recommender systems can outperform the sophisticated ones with limited information resources. Two possible strategies are suggested in this thesis to achieve the proposed information enrichment for recommenders: • The first strategy suggests that information resources can be enriched by considering other information or data facets. Specifically, a taxonomy-based recommender, Hybrid Taxonomy Recommender (HTR), is presented in this thesis. HTR exploits the relationship between users’ taxonomic preferences and item preferences from the combination of the widely available product taxonomic information and the existing user rating data, and it then utilises this taxonomic preference to item preference relation to generate high quality recommendations. • The second strategy suggests that information resources can be enriched simply by obtaining information resources from other parties. In this thesis, a distributed recommender framework, Ecommerce-oriented Distributed Recommender System (EDRS), is proposed. The proposed EDRS allows multiple recommenders from different parties (i.e. organisations or ecommerce sites) to share recommendations and information resources with each other in order to improve their recommendation quality. Based on the results obtained from the experiments conducted in this thesis, the proposed systems and techniques have achieved great improvement in both making quality recommendations and alleviating the cold-start problem.

Reasoning with sorted-pareto dominance and other qualitative and partially ordered preferences in soft constraints

In decision making problems where we need to choose a particular decision or alternative from a set of possible choices, we often have some preferences which determine if we prefer one decision over another. When these preferences give us an ordering on the decisions that is complete, then it is easy to choose the best or one of the best decisions. However it often occurs that the preferences relation is partially ordered, and we have no best decision. In this thesis, we look at what happens when we have such a partial order over a set of decisions, in particular when we have multiple orderings on a set of decisions, and we present a framework for qualitative decision making. We look at the different natural notions of optimal decision that occur in this framework, which gives us different optimality classes, and we examine the relationships between these classes. We then look in particular at a qualitative preference relation called Sorted-Pareto Dominance, which is an extension of Pareto Dominance, and we give a semantics for this relation as one that is compatible with any order-preserving mapping of an ordinal preference scale to a numerical one. We apply Sorted-Pareto dominance to a Soft Constraints setting, where we solve problems in which the soft constraints associate qualitative preferences to decisions in a decision problem. We also examine the Sorted-Pareto dominance relation in the context of our qualitative decision making framework, looking at the relevant optimality classes for the Sorted-Pareto case, which gives us classes of decisions that are necessarily optimal, and optimal for some choice of mapping of an ordinal scale to a quantitative one. We provide some empirical analysis of Sorted-Pareto constraints problems and examine the optimality classes that result.

Reasoning with imprecise trade-offs in decision making under certainty and uncertainty

In many real world situations, we make decisions in the presence of multiple, often conflicting and non-commensurate objectives. The process of optimizing systematically and simultaneously over a set of objective functions is known as multi-objective optimization. In multi-objective optimization, we have a (possibly exponentially large) set of decisions and each decision has a set of alternatives. Each alternative depends on the state of the world, and is evaluated with respect to a number of criteria. In this thesis, we consider the decision making problems in two scenarios. In the first scenario, the current state of the world, under which the decisions are to be made, is known in advance. In the second scenario, the current state of the world is unknown at the time of making decisions. For decision making under certainty, we consider the framework of multiobjective constraint optimization and focus on extending the algorithms to solve these models to the case where there are additional trade-offs. We focus especially on branch-and-bound algorithms that use a mini-buckets algorithm for generating the upper bound at each node of the search tree (in the context of maximizing values of objectives). Since the size of the guiding upper bound sets can become very large during the search, we introduce efficient methods for reducing these sets, yet still maintaining the upper bound property. We define a formalism for imprecise trade-offs, which allows the decision maker during the elicitation stage, to specify a preference for one multi-objective utility vector over another, and use such preferences to infer other preferences. The induced preference relation then is used to eliminate the dominated utility vectors during the computation. For testing the dominance between multi-objective utility vectors, we present three different approaches. The first is based on a linear programming approach, the second is by use of distance-based algorithm (which uses a measure of the distance between a point and a convex cone); the third approach makes use of a matrix multiplication, which results in much faster dominance checks with respect to the preference relation induced by the trade-offs. Furthermore, we show that our trade-offs approach, which is based on a preference inference technique, can also be given an alternative semantics based on the well known Multi-Attribute Utility Theory. Our comprehensive experimental results on common multi-objective constraint optimization benchmarks demonstrate that the proposed enhancements allow the algorithms to scale up to much larger problems than before. For decision making problems under uncertainty, we describe multi-objective influence diagrams, based on a set of p objectives, where utility values are vectors in Rp, and are typically only partially ordered. These can be solved by a variable elimination algorithm, leading to a set of maximal values of expected utility. If the Pareto ordering is used this set can often be prohibitively large. We consider approximate representations of the Pareto set based on ϵ-coverings, allowing much larger problems to be solved. In addition, we define a method for incorporating user trade-offs, which also greatly improves the efficiency.

A defeasible reasoning model of inductive concept learning from examples and communication

This paper introduces a logical model of inductive generalization, and specifically of the machine learning task of inductive concept learning (ICL). We argue that some inductive processes, like ICL, can be seen as a form of defeasible reasoning. We define a consequence relation characterizing which hypotheses can be induced from given sets of examples, and study its properties, showing they correspond to a rather well-behaved non-monotonic logic. We will also show that with the addition of a preference relation on inductive theories we can characterize the inductive bias of ICL algorithms. The second part of the paper shows how this logical characterization of inductive generalization can be integrated with another form of non-monotonic reasoning (argumentation), to define a model of multiagent ICL. This integration allows two or more agents to learn, in a consistent way, both from induction and from arguments used in the communication between them. We show that the inductive theories achieved by multiagent induction plus argumentation are sound, i.e. they are precisely the same as the inductive theories built by a single agent with all data. © 2012 Elsevier B.V.

Core Retionalizability in Two-Agent Exchange Economies.

We provide a characterization of selection correspondences in two-person exchange economies that can be core rationalized in the sense that there exists a preference profile with some standard properties that generates the observed choices as the set of core elements of the economy for any given initial endowment vector. The approach followed in this paper deviates from the standard rational choice model in that a rationalization in terms of a profile of individual orderings rather than in terms of a single individual or social preference relation is analyzed.

Top trading with fixed tie-breaking in markets with indivisible goods

We study markets with indivisible goods where monetary compensations are not possible. Each individual is endowed with an object and a preference relation over all objects. When preferences are strict, Gale's top trading cycle algorithm finds the unique core allocation. When preferences are not necessarily strict, we use an exogenous profile of tie-breakers to resolve any ties in individuals' preferences and apply Gale's top trading cycle algorithm for the resulting profile of strict preferences. We provide a foundation of these simple extensions of Gale's top trading cycle algorithm from strict preferences to weak preferences. We show that Gale's top trading cycle algorithm with fixed tie-breaking is characterized by individual rationality, strategy-proofness, weak efficiency, non-bossiness, and consistency. Our result supports the common practice in applications to break ties in weak preferences using some fixed exogenous criteria and then to use a 'good and simple' rule for the resulting strict preferences. This reinforces the market-based approach even in the presence of indifferences because always competitive allocations are chosen.

Single-basined choice

Single-basined preferences generalize single-dipped preferences by allowing for multiple worst elements. These preferences have played an important role in areas such as voting, strategy-proofness and matching problems. We examine the notion of single-basinedness in a choice-theoretic setting. In conjunction with independence of irrelevant alternatives, single-basined choice implies a structure that conforms to the motivation underlying our definition. We also establish the consequenes of requiring single-basined choice correspondences to be upper semicontinuous, and of the revealed preference relation to be Suzumura consistent. Journal of Economic Literature.

Penalty functions over a cartesian product of lattices

In this work we present the concept of penalty function over a Cartesian product of lattices. To build these mappings, we make use of restricted dissimilarity functions and distances between fuzzy sets. We also present an algorithm that extends the weighted voting method for a fuzzy preference relation.

Desempenho dos serviços de atenção primária do município de Natal/RN para o diagnóstico e controle da tuberculose

Tuberculosis (TB) is one of the most important health problems being faced worldwide. In Brazil, the responsibility for the actions of to diagnosis and control of this disease was transferred to the municipalities within the Primary Health Care (PHC), aiming at improvement in epidemiological indicators, requiring reorientation of the practice of family health teams and requiring methodologies to analyze the extent to which components of the PHC are being achieved. Thus, this study aims to analyze the performance of primary care services in the city of Natal-RN for the diagnosis and control of TB, from the perspective of health professionals (doctors and nurses). The study is descriptive, cross-sectional and quantitative. Data collection was conducted from March to July 2011 and involved 121 health professionals working in 52 health units (family health unit, basic health unit and mixed units). The instrument is structured based on the Primary Care Assessment Tool (PCAT), validated and adapted to assess attention to TB in Brazil, and includes questions regarding the Structure and Process components of health services. For quantitative analysis, it was constructed indicators, whose response patterns are followed according to the Likert scale between one and five, which meant the degree of preference relation (or agreement) of the claims. Values between 1 and 3 were considered unsatisfactory for the indicator, between 3 and less than 4, regular, and between 4 and 5, satisfactory. With regard to inputs and equipment, the units had satisfactory condition for form (  = 4.26), consultation (  = 4.02) and basic basket (  = 4.24); regular condition to pot (  = 3.56) and unsatisfactory conditions for transportation tickets (  = 1.50) and sputum smear microscopy (  = 2.42) and X-rays (  = 1.07). In relation to actions, there was satisfactory development for those focused on the individual patient. Actions aimed at the collective level, as the search for respiratory symptoms (RS), monitoring of contacts and guidelines for the community ranged from regular to unsatisfactory (  = 3.16 -  = 1.34). With regard to training, 94,2% received training to identify RS. As regards the time for diagnosis, the median time elapsed between the identification of RS and the beginning of treatment it was 22 days. In relation to the difficulties faced by professionals in the diagnosis of TB, 56,2% reported that they are related only to health services, especially for the failure in the rearguard laboratory and in the specialized services reference, the lack of human and material resources and low performing an active search. The professionals perceive the performance of diagnosis and control of TB, permeated with limitations and barriers to organizational and operational character of various sizes, emerging the need for effective coordination of various sectors and key stakeholders of TB care, to adoption of a new intersectoral strategies that aim to increase the responsiveness of the PHC, providing the best performance in service delivery to the user, family and community, and ensuring effective action and resolving the needs of this population group.

As ações de controle da tuberculose na atenção primária à saúde: a visão do doente

Tuberculosis is a disease of great impact on the world context today. In Brazil, the disease management was directed to the Primary Health Care, due to the determination of the Ministry of Health to decentralize health actions for primary care. Thus, since the actions of diagnosis, treatment and control of the disease should happen in this context, however, there are still many barriers that may hinder the realization of these determinations. This study aims to analyze the development of tuberculosis control activities conducted in the services of primary health care from the patient's vision. This is a descriptive, cross-sectional and quantitative study. The population consists of 517 tuberculosis patients treated in units of Primary Health Care in the city of Natal-RN; the sample consists of 93 TB patients. The collect instrument is structured, based in The Primary Care Assessment Tool (PCAT), validated in Brazil and adapted to assess attention to TB in Brazil, with modifications. This instrument was divided into blocks: the first one describes the socio-demographic information of patients with TB and the second one describes the health services working in control, diagnosis and treatment of TB, and includes issues related to the dimensions of primary care: access, bond, services, coordination of care, guidance to the community and family focus. For quantitative analysis, were built indicators for each item of the instrument. The response patterns are followed according to the Likert scale, which was assigned a value between one and five meant that the degree of preference relation (or agreement) of the statements. Values between 1 and 3 were considered unsatisfactory for the indicator, between 3 and less than 4, regular, and between 4 and 5, satisfactory. The results indicate that 62.37% of patients are male, 27.96% aged 41 to 50 years old, and 34.41% unemployed, with low education and low family income. It was found that the reference hospital services are the front door to the patient (59.14%), and are also the local diagnosis of the disease (72.04%). On access, the conditions satisfactory found are: the number of times the patients need to pick up the health care issue, the marking and the facility to get a consultancy in the HS, assistance provided without harm to the individual's attendance labor and facilities related to the proximity between the residence and services; were considered unsatisfactory conditions related to travel to the HS, and on hours and days of operation of services. As for the cast of services were satisfactory and regular actions related to the request for examination to become viable in the first HS, the availability of pot to perform smear and medicines for the treatment, as well as consultations control and receiving information about the disease and the treatment performed; it is considered unsatisfactory the performance of the home care for patients with TB by the HS that acts as a front door, for implementation of the Directly Observed Treatment (DOT), home visits during treatment, the provision of transportation allowance to the patient and the existence of groups for TB patients. Regarding the coordination of care, resulted in regular the action of referring the patient to other HS to obtain examinations, and as unsatisfactory referral to obtain medications. The relationship bond between patient and health team were considered satisfactory in the majority or regular. As for the family and community focus, is satisfactory only the indicator relating to questions from professionals to the patient about the existence of respiratory symptoms in the family. It is considered that there is need for greater commitment from government entities to the incentives required to TB control, as well as the availability of necessary inputs and training of human resources working in the PHC in the ongoing quest to strengthen primary care, as a place of broader host needs to contact the user with the actions and health professionals. It is recommended the adoption of management mechanisms possible to expand the capacity of the health PHC, promoting the service delivery to the user and ensuring attention to population health.