An overview of recommender systems in the healthy food domain

Recently, food recommender systems have received increasing attention due to their relevance for healthy living. Most existing studies on the food domain focus on recommendations that suggest proper food items for individual users on the basis of considering their preferences or health problems. These systems also provide functionalities to keep track of nutritional consumption as well as to persuade users to change their eating behavior in positive ways. Also, group recommendation functionalities are very useful in the food domain, especially when a group of users wants to have a dinner together at home or have a birthday party in a restaurant. Such scenarios create many challenges for food recommender systems since the preferences of all group members have to be taken into account in an adequate fashion. In this paper, we present an overview of recommendation techniques for individuals and groups in the healthy food domain. In addition, we analyze the existing state-of-the-art in food recommender systems and discuss research challenges related to the development of future food recommendation technologies.     

Recommender systems in the healthcare domain: state-of-the-art and research issues

Journal of Intelligent Information Systems - Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their...     

Explanations for over-constrained problems using QuickXPlain with speculative executions

Conflict detection is used in various scenarios ranging from interactive decision making (e.g., knowledge-based configuration) to the diagnosis of potentially faulty models (e.g., using knowledge base analysis operations). Conflicts can be regarded as sets of restrictions (constraints) causing an inconsistency. Junker’s QuickXPlain is a divide-and-conquer based algorithm for the detection of preferred minimal conflicts. In this article, we present a novel approach to the detection of such conflicts which is based on speculative programming. We introduce a parallelization of QuickXPlain and empirically evaluate this approach on the basis of synthesized knowledge bases representing feature models. The results of this evaluation show significant performance improvements in the parallelized QuickXPlain version.

     

Endogenous heparin-like substances significantly impair coagulation in patients undergoing orthotopic liver transplantation

Orthotopic liver transplantation (OLT) is associated with severe bleeding, especially after reperfusion of the grafted liver. Heparin released from the liver graft contributes to postreperfusion coagulopathy. Although patients with liver cirrhosis have increased levels of endogenous heparinoids, the role of these substances during liver transplantation is unclear. Therefore, we performed native and heparinase-modified thrombelastography (TEG) in 72 patients undergoing OLT. TEG was performed at skin incision, 10 min before and 10 min after clamping of the vena cava, 10 min before and 10 min after graft perfusion, and at the end of surgery. Heparinase-modified TEG compared with native TEG demonstrated heparin activity. In contrast to other investigations, we found significant heparin effects before reperfusion, although patients received no exogenous heparin. These heparin effects were greater in patients with cirrhosis compared with patients with cancer as the underlying disease leading to OLT. Administration of coagulation factors is the usual treatment of coagulopathies during OLT. The comparison of native versus heparinase-modified TEG can distinguish between heparin activity or coagulation factor deficiency as a cause of bleeding complications and provides a rational approach to the treatment of bleeding during OLT. Implications: Impaired coagulation function, contributed to by heparin or heparin-like substances, is frequently observed after reperfusion of a transplanted liver. This study demonstrates that a heparinase-modified thrombelastography can identify significant heparin effects in the absence of exogenous heparin administration in patients undergoing liver transplantation.     

Constraint-based and SAT-based diagnosis of automotive configuration problems

We compare the concepts and computation of optimized diagnoses in the context of Boolean constraint based knowledge systems of automotive configuration, namely the preferred minimal diagnosis and the minimum weighted diagnosis. In order to restore the consistency of an over-constrained system w.r.t. a strict total order of the user requirements, the preferred minimal diagnosis tries to keep the most preferred user requirements and can be computed, for example, by the FASTDIAG algorithm. In contrast, partial weighted MinUNSAT solvers aim to find a set of unsatisfied clauses with the minimum sum of weights, such that the diagnosis is of minimum weight. It turns out that both concepts have similarities, i.e., both deliver an optimal minimal correction subset. We show use cases from automotive configuration where optimized diagnoses are desired. We point out theoretical commonalities and prove the reducibility of both concepts to each other, i.e., both problems are FP^NP-complete, which was an open question. In addition to exact algorithms we present greedy algorithms. We evaluate the performance of exact and greedy algorithms on problem instances based on real automotive configuration data from three different German car manufacturers, and we compare the time and quality tradeoff.     

Consistency-based diagnosis of configuration knowledge bases

Configuration problems are a thriving application area for declarative knowledge representation that currently experiences a constant increase in size and complexity of knowledge bases. Automated support of the debugging process of such knowledge bases is a necessary prerequisite for effective development of configurators. We show that this task can be achieved by consistency-based diagnosis techniques. Based on the formal definition of consistency-based configuration we develop a framework suitable for diagnosing configuration knowledge bases. During the test phase of configurators, valid and invalid examples are used to test the correctness of the system. In case such examples lead to unintended results, debugging of the knowledge base is initiated. Starting from a clear definition of diagnosis in the configuration domain we develop an algorithm based on conflicts. Our framework is general enough for its adaptation to diagnosing customer requirements to identify unachievable conditions during configuration sessions.A prototype implementation using commercial constraint-based configurator libraries shows the feasibility of diagnosis within the tight time bounds of interactive debugging sessions. Finally, we discuss the usefulness of the outcomes of the diagnostic process in different scenarios.     

Guest Editors' Introduction: Recommender Systems

This special issue presents eight articles, five long and three short, on techniques to improve recommender systems. They cover improving such aspects as user interaction with recommenders, the quality of results presented to users, and user trust in presented recommendations. This article is part of a special issue on Recommender Systems.     

Knowledge representations for the interactive selling of financial services

Selling financial services requires deep knowledge about the product domain as well as about potential wishes and needs of customers. In the financial services domain (especially in the retail sector) sales representatives can differ greatly in their expertise and level of sales knowledge. Therefore financial service providers ask for tools effectively supporting sales representatives in the dialog with the customer. In this paper we present technologies which allow a flexible mapping of product, marketing and sales knowledge to the representation of a recommender knowledge-base thus providing an infrastructure for the interactive selling of financial services. Furthermore, we report experiences gained from financial service recommender development projects.

Anytime diagnosis for reconfiguration

Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FlexDiag which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FlexDiag helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy.