scholarly journals Effect of Collaborative Recommender System Parameters: Common Set Cardinality and the Similarity Measure

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Mohammad Yahya H. Al-Shamri
Keyword(s):  
Similarity Measure ◽  
Recommender System ◽  
System Performance ◽  
Similarity Measures ◽  
Mean Difference ◽  
User Profiles ◽  
System Parameters ◽  
The Mean ◽  
Common Items ◽  
Evaluation Metric

Recommender systems are widespread due to their ability to help Web users surf the Internet in a personalized way. For example, collaborative recommender system is a powerful Web personalization tool for suggesting many useful items to a given user based on opinions collected from his neighbors. Among many, similarity measure is an important factor affecting the performance of the collaborative recommender system. However, the similarity measure itself largely depends on the overlapping between the user profiles. Most of the previous systems are tested on a predefined number of common items and neighbors. However, the system performance may vary if we changed these parameters. The main aim of this paper is to examine the performance of the collaborative recommender system under many similarity measures, common set cardinalities, rating mean groups, and neighborhood set sizes. For this purpose, we propose a modified version for the mean difference weight similarity measure and a new evaluation metric called users’ coverage for measuring the recommender system ability for helping users. The experimental results show that the modified mean difference weight similarity measure outperforms other similarity measures and the collaborative recommender system performance varies by varying its parameters; hence we must specify the system parameters in advance.

scholarly journals 3D Facial Similarity Measure Based on Geodesic Network and Curvatures

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Junli Zhao ◽  
Cuiting Liu ◽  
Zhongke Wu ◽  
Fuqing Duan ◽  
Minqi Zhang ◽  
...  
Keyword(s):  
Similarity Measure ◽  
Subjective Evaluation ◽  
Similarity Measures ◽  
Weighted Average ◽  
Correlation Coefficients ◽  
Shape Index ◽  
3D Face ◽  
The Mean ◽  
Facial Similarity ◽  
Face Similarity

Automated 3D facial similarity measure is a challenging and valuable research topic in anthropology and computer graphics. It is widely used in various fields, such as criminal investigation, kinship confirmation, and face recognition. This paper proposes a 3D facial similarity measure method based on a combination of geodesic and curvature features. Firstly, a geodesic network is generated for each face with geodesics and iso-geodesics determined and these network points are adopted as the correspondence across face models. Then, four metrics associated with curvatures, that is, the mean curvature, Gaussian curvature, shape index, and curvedness, are computed for each network point by using a weighted average of its neighborhood points. Finally, correlation coefficients according to these metrics are computed, respectively, as the similarity measures between two 3D face models. Experiments of different persons’ 3D facial models and different 3D facial models of the same person are implemented and compared with a subjective face similarity study. The results show that the geodesic network plays an important role in 3D facial similarity measure. The similarity measure defined by shape index is consistent with human’s subjective evaluation basically, and it can measure the 3D face similarity more objectively than the other indices.

Selecting a text similarity measure for a content-based recommender system

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Manjula Wijewickrema ◽  
Vivien Petras ◽  
Naomal Dias
Keyword(s):  
Social Sciences ◽  
Similarity Measure ◽  
Recommender System ◽  
Similarity Measures ◽  
Cosine Similarity ◽  
Text Similarity ◽  
Content Type ◽  
Work Related ◽  
Cosine Similarity Measures ◽  
The Impact

Purpose The purpose of this paper is to develop a journal recommender system, which compares the content similarities between a manuscript and the existing journal articles in two subject corpora (covering the social sciences and medicine). The study examines the appropriateness of three text similarity measures and the impact of numerous aspects of corpus documents on system performance. Design/methodology/approach Implemented three similarity measures one at a time on a journal recommender system with two separate journal corpora. Two distinct samples of test abstracts were classified and evaluated based on the normalized discounted cumulative gain. Findings The BM25 similarity measure outperforms both the cosine and unigram language similarity measures overall. The unigram language measure shows the lowest performance. The performance results are significantly different between each pair of similarity measures, while the BM25 and cosine similarity measures are moderately correlated. The cosine similarity achieves better performance for subjects with higher density of technical vocabulary and shorter corpus documents. Moreover, increasing the number of corpus journals in the domain of social sciences achieved better performance for cosine similarity and BM25. Originality/value This is the first work related to comparing the suitability of a number of string-based similarity measures with distinct corpora for journal recommender systems.

scholarly journals An Item–Item Collaborative Filtering Recommender System Using Trust and Genre to Address the Cold-Start Problem

2019 ◽  
Vol 3 (3) ◽  
pp. 39 ◽  
Author(s):  
Mahamudul Hasan ◽  
Falguni Roy
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recommender System ◽  
Similarity Measures ◽  
Cold Start ◽  
Absolute Error ◽  
Prediction Algorithm ◽  
Rating Data ◽  
Asymmetric Correlation ◽  
Cold Start Problem

Item-based collaborative filtering is one of the most popular techniques in the recommender system to retrieve useful items for the users by finding the correlation among the items. Traditional item-based collaborative filtering works well when there exists sufficient rating data but cannot calculate similarity for new items, known as a cold-start problem. Usually, for the lack of rating data, the identification of the similarity among the cold-start items is difficult. As a result, existing techniques fail to predict accurate recommendations for cold-start items which also affects the recommender system’s performance. In this paper, two item-based similarity measures have been designed to overcome this problem by incorporating items’ genre data. An item might be uniform to other items as they might belong to more than one common genre. Thus, one of the similarity measures is defined by determining the degree of direct asymmetric correlation between items by considering their association of common genres. However, the similarity is determined between a couple of items where one of the items could be cold-start and another could be any highly rated item. Thus, the proposed similarity measure is accounted for as asymmetric by taking consideration of the item’s rating data. Another similarity measure is defined as the relative interconnection between items based on transitive inference. In addition, an enhanced prediction algorithm has been proposed so that it can calculate a better prediction for the recommendation. The proposed approach has experimented with two popular datasets that is Movielens and MovieTweets. In addition, it is found that the proposed technique performs better in comparison with the traditional techniques in a collaborative filtering recommender system. The proposed approach improved prediction accuracy for Movielens and MovieTweets approximately in terms of 3.42% & 8.58% mean absolute error, 7.25% & 3.29% precision, 7.20% & 7.55% recall, 8.76% & 5.15% f-measure and 49.3% and 16.49% mean reciprocal rank, respectively.

MATHURA (MBI) - A NOVEL IMPUTATION MEASURE FOR IMPUTATION OF MISSING VALUES IN MEDICAL DATASETS

2019 ◽  
Vol 13 ◽  
Author(s):  
B. Mathura Bai ◽  
N. Mangathayaru ◽  
B. Padmaja Rani ◽  
Shadi Aljawarneh
Keyword(s):  
Similarity Measure ◽  
Medical Records ◽  
Missing Values ◽  
Similarity Measures ◽  
Common Problems ◽  
Experiment Analysis

: Missing attribute values in medical datasets are one of the most common problems faced when mining medical datasets. Estimation of missing values is a major challenging task in pre-processing of datasets. Any wrong estimate of missing attribute values can lead to inefficient and improper classification thus resulting in lower classifier accuracies. Similarity measures play a key role during the imputation process. The use of an appropriate and better similarity measure can help to achieve better imputation and improved classification accuracies. This paper proposes a novel imputation measure for finding similarity between missing and non-missing instances in medical datasets. Experiments are carried by applying both the proposed imputation technique and popular benchmark existing imputation techniques. Classification is carried using KNN, J48, SMO and RBFN classifiers. Experiment analysis proved that after imputation of medical records using proposed imputation technique, the resulting classification accuracies reported by the classifiers KNN, J48 and SMO have improved when compared to other existing benchmark imputation techniques.

scholarly journals Do we need to improve the reporting of evidence in tendinopathy management? A critical appraisal of systematic reviews with recommendations on strength of evidence assessment

2021 ◽  
Vol 7 (1) ◽  
pp. e000920
Author(s):  
Dimitris Challoumas ◽  
Neal L Millar
Keyword(s):  
Systematic Reviews ◽  
Assessment Tool ◽  
Data Extraction ◽  
Cochrane Collaboration ◽  
Mean Difference ◽  
Strength Of Evidence ◽  
The Mean ◽  
Randomised Controlled ◽  
Meta Analyses ◽  
The Cochrane Collaboration

ObjectiveTo critically appraise the quality of published systematic reviews (SRs) of randomised controlled trials (RCTs) in tendinopathy with regard to handling and reporting of results with special emphasis on strength of evidence assessment.Data sourcesMedline from inception to June 2020.Study eligibilityAll SRs of RCTs assessing the effectiveness of any intervention(s) on any location of tendinopathy.Data extraction and synthesisIncluded SRs were appraised with the use of a 12-item tool devised by the authors arising from the Preferred Reporting Items in Systematic Reviews and Meta-Analyses statement and other relevant guidance. Subgroup analyses were performed based on impact factor (IF) of publishing journals and date of publication.ResultsA total of 57 SRs were included published in 38 journals between 2006 and 2020. The most commonly used risk-of-bias (RoB) assessment tool and strength of evidence assessment tool were the Cochrane Collaboration RoB tool and the Cochrane Collaboration Back Review Group tool, respectively. The mean score on the appraisal tool was 46.5% (range 0%–100%). SRs published in higher IF journals (>4.7) were associated with a higher mean score than those in lower IF journals (mean difference 26.4%±8.8%, p=0.004). The mean score of the 10 most recently published SRs was similar to that of the first 10 published SRs (mean difference 8.3%±13.7%, p=0.54). Only 23 SRs (40%) used the results of their RoB assessment in data synthesis and more than half (n=30; 50%) did not assess the strength of evidence of their results. Only 12 SRs (21%) assessed their strength of evidence appropriately.ConclusionsIn light of the poor presentation of evidence identified by our review, we provide recommendations to increase transparency and reproducibility in future SRs.

scholarly journals The Sankara Nethralaya Tamil Nadu Essilor Myopia (STEM) Study—Defining a Threshold for Non-Cycloplegic Myopia Prevalence in Children

2021 ◽  
Vol 10 (6) ◽  
pp. 1215
Author(s):  
Aparna Gopalakrishnan ◽  
Jameel Rizwana Hussaindeen ◽  
Viswanathan Sivaraman ◽  
Meenakshi Swaminathan ◽  
Yee Ling Wong ◽  
...  
Keyword(s):  
Open Field ◽  
Tamil Nadu ◽  
Mean Difference ◽  
School Vision ◽  
Elite School ◽  
Prevalence Estimates ◽  
The Mean ◽  
Clinically Significant ◽  
Set Up ◽  
Definition Of

The aim of this study was to investigate the agreement between cycloplegic and non-cycloplegic autorefraction with an open-field auto refractor in a school vision screening set up, and to define a threshold for myopia that agrees with the standard cycloplegic refraction threshold. The study was conducted as part of the Sankara Nethralaya Tamil Nadu Essilor Myopia (STEM) study, which investigated the prevalence, incidence, and risk factors for myopia among children in South India. Children from two schools aged 5 to 15 years, with no ocular abnormalities and whose parents gave informed consent for cycloplegic refraction were included in the study. All the children underwent visual acuity assessment (Pocket Vision Screener, Elite school of Optometry, India), followed by non-cycloplegic and cycloplegic (1% tropicamide) open-field autorefraction (Grand Seiko, WAM-5500). A total of 387 children were included in the study, of whom 201 were boys. The mean (SD) age of the children was 12.2 (±2.1) years. Overall, the mean difference between cycloplegic and non-cycloplegic spherical equivalent (SE) open-field autorefraction measures was 0.34 D (limits of agreement (LOA), 1.06 D to −0.38 D). For myopes, the mean difference between cycloplegic and non-cycloplegic SE was 0.13 D (LOA, 0.63D to −0.36D). The prevalence of myopia was 12% (95% CI, 8% to 15%) using the threshold of cycloplegic SE ≤ −0.50 D, and was 14% (95% CI, 11% to 17%) with SE ≤ −0.50 D using non-cycloplegic refraction. When myopia was defined as SE of ≤−0.75 D under non-cycloplegic conditions, there was no difference between cycloplegic and non-cycloplegic open-field autorefraction prevalence estimates (12%; 95% CI, 8% to 15%; p = 1.00). Overall, non-cycloplegic refraction underestimates hyperopia and overestimates myopia; but for subjects with myopia, this difference is minimal and not clinically significant. A threshold of SE ≤ −0.75 D agrees well for the estimation of myopia prevalence among children when using non-cycloplegic refraction and is comparable with the standard definition of cycloplegic myopic refraction of SE ≤ −0.50 D.

scholarly journals Estimates of the mean difference in orthopaedic randomized trials: obligatory yet obscure

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lauri Raittio ◽  
Antti Launonen ◽  
Ville M. Mattila ◽  
Aleksi Reito
Keyword(s):  
Primary Outcome ◽  
Outcome Measure ◽  
Patient Reported Outcome ◽  
Ceiling Effect ◽  
Mean Difference ◽  
Discussion Section ◽  
Power Calculations ◽  
Randomized Controlled ◽  
Patient Reported ◽  
The Mean

Abstract Background Randomized controlled trials in orthopaedics are powered to mainly find large effect sizes. A possible discrepancy between the estimated and the real mean difference is a challenge for statistical inference based on p-values. We explored the justifications of the mean difference estimates used in power calculations. The assessment of distribution of observations in the primary outcome and the possibility of ceiling effects were also assessed. Methods Systematic review of the randomized controlled trials with power calculations in eight clinical orthopaedic journals published between 2016 and 2019. Trials with one continuous primary outcome and 1:1 allocation were eligible. Rationales and references for the mean difference estimate were recorded from the Methods sections. The possibility of ceiling effect was addressed by the assessment of the weighted mean and standard deviation of the primary outcome and its elaboration in the Discussion section of each RCT where available. Results 264 trials were included in this study. Of these, 108 (41 %) trials provided some rationale or reference for the mean difference estimate. The most common rationales or references for the estimate of mean difference were minimal clinical important difference (16 %), observational studies on the same subject (8 %) and the ‘clinical relevance’ of the authors (6 %). In a third of the trials, the weighted mean plus 1 standard deviation of the primary outcome reached over the best value in the patient-reported outcome measure scale, indicating the possibility of ceiling effect in the outcome. Conclusions The chosen mean difference estimates in power calculations are rarely properly justified in orthopaedic trials. In general, trials with a patient-reported outcome measure as the primary outcome do not assess or report the possibility of the ceiling effect in the primary outcome or elaborate further in the Discussion section.

scholarly journals Corneal Biomechanical Parameters and Central Corneal Thickness in Glaucoma Patients, Glaucoma Suspects, and a Healthy Population

2021 ◽  
Vol 10 (12) ◽  
pp. 2637
Author(s):  
Mª. Ángeles del Buey-Sayas ◽  
Elena Lanchares-Sancho ◽  
Pilar Campins-Falcó ◽  
María Dolores Pinazo-Durán ◽  
Cristina Peris-Martínez
Keyword(s):  
Central Corneal Thickness ◽  
Corneal Thickness ◽  
Mean Difference ◽  
The Other ◽  
Control Group ◽  
Healthy Population ◽  
Diagnostic Categories ◽  
Significant Difference ◽  
Biomechanical Parameters ◽  
The Mean

Purpose: To evaluate and compare corneal hysteresis (CH), corneal resistance factor (CRF), and central corneal thickness (CCT), measurements were taken between a healthy population (controls), patients diagnosed with glaucoma (DG), and glaucoma suspect patients due to ocular hypertension (OHT), family history of glaucoma (FHG), or glaucoma-like optic discs (GLD). Additionally, Goldmann-correlated intraocular pressure (IOPg) and corneal-compensated IOP (IOPcc) were compared between the different groups of patients. Methods: In this prospective analytical-observational study, a total of 1065 patients (one eye of each) were recruited to undergo Ocular Response Analyzer (ORA) testing, ultrasound pachymetry, and clinical examination. Corneal biomechanical parameters (CH, CRF), CCT, IOPg, and IOPcc were measured in the control group (n = 574) and the other groups: DG (n = 147), FHG (n = 78), GLD (n = 90), and OHT (n = 176). We performed a variance analysis (ANOVA) for all the dependent variables according to the different diagnostic categories with multiple comparisons to identify the differences between the diagnostic categories, deeming p < 0.05 as statistically significant. Results: The mean CH in the DG group (9.69 mmHg) was significantly lower compared to controls (10.75 mmHg; mean difference 1.05, p < 0.001), FHG (10.70 mmHg; mean difference 1.00, p < 0.05), GLD (10.63 mmHg; mean difference 0.93, p < 0.05) and OHT (10.54 mmHg; mean difference 0.84, p < 0.05). No glaucoma suspects (FHG, GLD, OHT groups) presented significant differences between themselves and the control group (p = 1.00). No statistically significant differences were found in the mean CRF between DG (11.18 mmHg) and the control group (10.75 mmHg; mean difference 0.42, p = 0.40). The FHG and OHT groups showed significantly higher mean CRF values (12.32 and 12.41 mmHg, respectively) than the DG group (11.18 mmHg), with mean differences of 1.13 (p < 0.05) and 1.22 (p < 0.001), respectively. No statistically significant differences were found in CCT in the analysis between DG (562 μ) and the other groups (control = 556 μ, FHG = 576 μ, GLD = 569 μ, OHT = 570 μ). The means of IOPg and IOPcc values were higher in the DG patient and suspect groups than in the control group, with statistically significant differences in all groups (p < 0.001). Conclusion: This study presents corneal biomechanical values (CH, CRF), CCT, IOPg, and IOPcc for diagnosed glaucoma patients, three suspected glaucoma groups, and a healthy population, using the ORA. Mean CH values were markedly lower in the DG group (diagnosed with glaucoma damage) compared to the other groups. No significant difference was found in CCT between the DG and control groups. Unexpectedly, CRF showed higher values in all groups than in the control group, but the difference was only statistically significant in the suspect groups (FHG, GLD, and OHT), not in the DG group.

scholarly journals A set theory based similarity measure for text clustering and classification

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ali A. Amer ◽  
Hassan I. Abdalla
Keyword(s):  
Set Theory ◽  
Similarity Measure ◽  
Similarity Measures ◽  
Text Clustering ◽  
Plagiarism Detection ◽  
K Nearest Neighbor ◽  
Single Measure ◽  
Highly Effective ◽  
Clustering And Classification ◽  
Effectiveness And Efficiency

Abstract Similarity measures have long been utilized in information retrieval and machine learning domains for multi-purposes including text retrieval, text clustering, text summarization, plagiarism detection, and several other text-processing applications. However, the problem with these measures is that, until recently, there has never been one single measure recorded to be highly effective and efficient at the same time. Thus, the quest for an efficient and effective similarity measure is still an open-ended challenge. This study, in consequence, introduces a new highly-effective and time-efficient similarity measure for text clustering and classification. Furthermore, the study aims to provide a comprehensive scrutinization for seven of the most widely used similarity measures, mainly concerning their effectiveness and efficiency. Using the K-nearest neighbor algorithm (KNN) for classification, the K-means algorithm for clustering, and the bag of word (BoW) model for feature selection, all similarity measures are carefully examined in detail. The experimental evaluation has been made on two of the most popular datasets, namely, Reuters-21 and Web-KB. The obtained results confirm that the proposed set theory-based similarity measure (STB-SM), as a pre-eminent measure, outweighs all state-of-art measures significantly with regards to both effectiveness and efficiency.

A New Equal Area Method to Calculate and Represent Physiologic, Anatomical, and Alveolar Dead Spaces

2006 ◽  
Vol 104 (4) ◽  
pp. 696-700 ◽  
Author(s):  
Yongquan Tang ◽  
Martin J. Turner ◽  
A Barry Baker
Keyword(s):  
Carbon Dioxide ◽  
Dead Space ◽  
Graphical Method ◽  
Mean Difference ◽  
Equal Area ◽  
Area Method ◽  
Physiologic Dead Space ◽  
Anatomical Dead Space ◽  
The Mean ◽  
The Difference

Background Physiologic dead space is usually estimated by the Bohr-Enghoff equation or the Fletcher method. Alveolar dead space is calculated as the difference between anatomical dead space estimated by the Fowler equal area method and physiologic dead space. This study introduces a graphical method that uses similar principles for measuring and displaying anatomical, physiologic, and alveolar dead spaces. Methods A new graphical equal area method for estimating physiologic dead space is derived. Physiologic dead spaces of 1,200 carbon dioxide expirograms obtained from 10 ventilated patients were calculated by the Bohr-Enghoff equation, the Fletcher area method, and the new graphical equal area method and were compared by Bland-Altman analysis. Dead space was varied by varying tidal volume, end-expiratory pressure, inspiratory-to-expiratory ratio, and inspiratory hold in each patient. Results The new graphical equal area method for calculating physiologic dead space is shown analytically to be identical to the Bohr-Enghoff calculation. The mean difference (limits of agreement) between the physiologic dead spaces calculated by the new equal area method and Bohr-Enghoff equation was -0.07 ml (-1.27 to 1.13 ml). The mean difference between new equal area method and the Fletcher area method was -0.09 ml (-1.52 to 1.34 ml). Conclusions The authors' equal area method for calculating, displaying, and visualizing physiologic dead space is easy to understand and yields the same results as the classic Bohr-Enghoff equation and Fletcher area method. All three dead spaces--physiologic, anatomical, and alveolar--together with their relations to expired volume, can be displayed conveniently on the x-axis of a carbon dioxide expirogram.

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