Search results for "Ranking"
showing 10 items of 212 documents
Tournament structure and nations' success in women's professional tennis
2007
The relationship between domestic professional tournament structure in women's tennis and the subsequent professional ranking success of a nation's female players is examined. The 2003 women's professional tennis tournament calendar provided the distribution of events in 33 countries. Criteria used to classify nations' success in women's professional tennis were as follows: number of players with Women's Tennis Association (WTA) points, number of players with Top 200 rankings, and the combined WTA ranking of a nation's Top 5 female players. Pearson product - moment correlations were performed between the number of tournaments and the three criteria. Considerable variation was observed in th…
How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm
2018
Most recommender systems suggest items that are popular among all users and similar to items a user usually consumes. As a result, the user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected i.e., serendipitous items. In this paper, we propose a serendipity-oriented, reranking algorithm called a serendipity-oriented greedy (SOG) algorithm, which improves serendipity of recommendations through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm, we employed the only publicly available datase…
Ranking-Oriented Collaborative Filtering: A Listwise Approach
2016
Collaborative filtering (CF) is one of the most effective techniques in recommender systems, which can be either rating oriented or ranking oriented. Ranking-oriented CF algorithms demonstrated significant performance gains in terms of ranking accuracy, being able to estimate a precise preference ranking of items for each user rather than the absolute ratings (as rating-oriented CF algorithms do). Conventional memory-based ranking-oriented CF can be referred to as pairwise algorithms. They represent each user as a set of preferences on each pair of items for similarity calculations and predictions. In this study, we propose ListCF, a novel listwise CF paradigm that seeks improvement in bot…
Learning Improved Feature Rankings through Decremental Input Pruning for Support Vector Based Drug Activity Prediction
2010
The use of certain machine learning and pattern recognition tools for automated pharmacological drug design has been recently introduced. Different families of learning algorithms and Support Vector Machines in particular have been applied to the task of associating observed chemical properties and pharmacological activities to certain kinds of representations of the candidate compounds. The purpose of this work, is to select an appropriate feature ordering from a large set of molecular descriptors usually used in the domain of Drug Activity Characterization. To this end, a new input pruning method is introduced and assessed with respect to commonly used feature ranking algorithms.
Projection Clustering Unfolding: A New Algorithm for Clustering Individuals or Items in a Preference Matrix
2020
In the framework of preference rankings, the interest can lie in clustering individuals or items in order to reduce the complexity of the preference space for an easier interpretation of collected data. The last years have seen a remarkable flowering of works about the use of decision tree for clustering preference vectors. As a matter of fact, decision trees are useful and intuitive, but they are very unstable: small perturbations bring big changes. This is the reason why it could be necessary to use more stable procedures in order to clustering ranking data. In this work, a Projection Clustering Unfolding (PCU) algorithm for preference data will be proposed in order to extract useful info…
Car style-holon recognition in computer-aided design
2019
Abstract Multi-scale design can presumably stimulate greater intelligence in computer-aided design (CAD). Using the style-holon concept, this paper proposes a computational approach to address multi-scale style recognition for automobiles. A style-holon is both a whole—it contains sub-styles of which it is composed—as well as a part of a broader style. In this paper, we first apply a variable precision rough set-based approach to car evaluation and ranking. Secondly, we extracted and subsequently computed the each car's characteristic lines from the CAD models. Finally, we identified style-holons using the property of a double-headed style-holon. A style-holon is necessarily included in a t…
European Energy Efficiency Evaluation Based on the Use of Super-Efficiency Under Undesirable Outputs in SBM Models
2020
Although Data Envelopment Analysis models have been intensively used for measuring efficiency, the inclusion of undesirable outputs has extended their use to analyse relevant fields such as environmental efficiency. In this context, slacks-based measure (SBM) models offer a remarkable alternative, largely due to their ability to deal with undesirable outputs. Additionally, super-efficiency evaluation in DEA is a useful complementary analysis for ranking the performance of efficient DMUs and even mandatory for dynamic efficiency evaluation. An extension to this approach in the presence of undesirable outputs is here introduced and then applied in the context of the environmental efficiency i…
Listwise Recommendation Approach with Non-negative Matrix Factorization
2018
Matrix factorization (MF) is one of the most effective categories of recommendation algorithms, which makes predictions based on the user-item rating matrix. Nowadays many studies reveal that the ultimate goal of recommendations is to predict correct rankings of these unrated items. However, most of the pioneering efforts on ranking-oriented MF predict users’ item ranking based on the original rating matrix, which fails to explicitly present users’ preference ranking on items and thus might result in some accuracy loss. In this paper, we formulate a novel listwise user-ranking probability prediction problem for recommendations, that aims to utilize a user-ranking probability matrix to predi…
Ranking of occupational health and safety risks by a multi-criteria perspective: Inclusion of human factors and application of VIKOR
2021
Nowadays, the Occupational Health and Safety (OHS) is more and more recognized as a crucial process to be properly managed and continuously improved by every organization. Primarily addressed to prevent workers’ injuries and diseases, it positively impacts on productivity, competitiveness and reputation as well as it con-tributes to cost savings in general. OHS management is grounded upon the risk assessment results, on the basis of which defining corrective measures to be taken to reduce risks to acceptable values. In this regard, the paper proposes a Multi-Criteria Decision Making (MCDM) based methodology addressed to the occupational risks prioritization. In order to overcome the shortco…
World`s Most Valuable Brand Resonation With Categories of Different Customer Needs
2017
One of the key performance indicators of brand success is its value. Brand value is an outcome of brand`s performance in market, and is largely depended from brand`s ability to satisfy certain customer needs. For the greatest success in the world`s market brand should resonate its ability to satisfy some of customer`s most universal needs. In this paper authors strives to find out which of the needs world`s most successful brands are resonating with. Therefore paper goal is to is to determine what customer needs world`s most valuable brands are primarily satisfying. First part of paper authors briefly evaluate Maslow theory of needs. In second part of paper authors identify main challenges …