Search results for "Information Retrieval"

Evaluation de la pertinence dans un système de recommandation sémantique de nouvelles économiques

2014

Today in the commercial and financial sectors, staying informed about economic news is crucial and involves targeting good articles to read, because the huge amount of information. To address this problem, we propose an innovative article recommendation system, based on the integration of a semantic description of articles and on a knowledge ontological model. We support our recommendation system on an intrinsically efficient vector model that we have perfected to overcome the confusion existing in models between the concepts of similarity and relevancy that does not take into account the effects of the difference in the accuracy of the semantic descriptions precision between profiles and a…

The Effect of the Range of a Modulating Phase Mask on the Retrieval of a Complex Object from Intensity Measurements

2021

The authors have been supported by the postdoctoral project (1.1.1.2/16/I/001, 1.1.1.2/VIAA/1/16/199), the CAMART2 project (grant agreement ID 739508), the Latvian Investment and Development Agency (LIDA) project (KC-PI-2017/105), and the grant for the Latvian State Emeritus Scientists.

Reputation and financial reporting in Finnish public organizations

2021

PurposeThis article analyzes the links between financial reports and reputation in the context of Finnish public sector organizations. In general, the paper discusses the accounting treatment of intangible and tangible assets and the quality and relevance of public sector financial reporting.Design/methodology/approachFor data, we combine three data sets: financial statement information of eight anonymous Finnish public organizations, the results of a reputation survey among their key stakeholders (N = 914) and a sample of the social media sentiment around the organizations.FindingsOur findings suggest that a decrease in spending and, surprisingly in the nonprofit sector, an increase in the…

An Automatic Ontology-Based Approach to Support Logical Representation of Observable and Measurable Data for Healthy Lifestyle Management: Proof-of-C…

2020

Background Lifestyle diseases, because of adverse health behavior, are the foremost cause of death worldwide. An eCoach system may encourage individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Such an eCoach system needs to collect and transform distributed heterogenous health and wellness data into meaningful information to train an artificially intelligent health risk prediction model. However, it may produce a data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between different heterogeneous networks, provide situation awareness, help in data integration, and discover…

Component search in a metaCASE environment

2001

Cognitive Linguistics as the Underlying Framework for Semantic Annotation

2012

In recent years many attempts have been made to design suitable sets of rules aimed at extracting the semantic meaning from plain text, and to achieve annotation, but very few approaches make extensive use of grammars. Current systems are mainly focused on extracting the semantic role of the entities described in the text. This approach has limitations: in such applications the semantic role is conceived merely as the meaning of the involved entities without considering their context. As an example, current semantic annotators often specify a date entity without any annotation regarding the kind of the date itself i.e. a birth date, a book publication date, and so on. Moreover, these system…

Generalizability and Simplicity as Criteria in Feature Selection: Application to Mood Classification in Music

2011

Classification of musical audio signals according to expressed mood or emotion has evident applications to content-based music retrieval in large databases. Wrapper selection is a dimension reduction method that has been proposed for improving classification performance. However, the technique is prone to lead to overfitting of the training data, which decreases the generalizability of the obtained results. We claim that previous attempts to apply wrapper selection in the field of music information retrieval (MIR) have led to disputable conclusions about the used methods due to inadequate analysis frameworks, indicative of overfitting, and biased results. This paper presents a framework bas…

A Cooperative Coevolution Framework for Parallel Learning to Rank

2015

We propose CCRank, the first parallel framework for learning to rank based on evolutionary algorithms (EA), aiming to significantly improve learning efficiency while maintaining accuracy. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures. Moreover, CC naturally allows parallelization of sub-solutions to the decomposed sub-problems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. We implement CCRank with three EA-based learning to rank algorithms for demonstration. E…

Investigating serendipity in recommender systems based on real user feedback

2018

Over the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-addressed question. According to the most common definition, serendipity consists of three components: relevance, novelty and unexpectedness, where each component has multiple variations. In this paper, we looked at eight different definitions of serendipity and asked users how they perceived them in the context of movie recommendations. We surveyed 475 users of the movie recommender system, MovieLens regarding 2146 movies in total and compared tho…

A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion

2015

Collaborative Filtering (CF) is one of the most successful algorithms in recommender systems. However, it suffers from data sparsity and scalability problems. Although many clustering techniques have been incorporated to alleviate these two problems, most of them fail to achieve further significant improvement in recommendation accuracy. First of all, most of them assume each user or item belongs to a single cluster. Since usually users can hold multiple interests and items may belong to multiple categories, it is more reasonable to assume that users and items can join multiple clusters (groups), where each cluster is a subset of like-minded users and items they prefer. Furthermore, most of…