scholarly journals Prosima: Protein similarity algorithm

2009 ◽  
Author(s):  
Tomas Novosad ◽  
Vaclav Snasel ◽  
Ajith Abraham ◽  
Jack Y Yang
Keyword(s):  
Similarity Algorithm

scholarly journals Event Detection through Lexical Chain Based Semantic Similarity Algorithm

2021 ◽  
Vol 1166 (1) ◽  
pp. 012016
Author(s):  
Swati Jain ◽  
Suraj Prakash Narayan ◽  
Nalini Meena ◽  
Rupesh Kumar Dewang ◽  
Utkarsh Bhartiya ◽  
...  
Keyword(s):  
Semantic Similarity ◽  
Event Detection ◽  
Similarity Algorithm ◽  
Lexical Chain

scholarly journals User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh B. Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
Correlation Coefficients ◽  
User Profile ◽  
Profile Data ◽  
Similarity Algorithm ◽  
Previous Algorithm ◽  
Movie Recommendation ◽  
Recommendation Accuracy

AbstractCollaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

scholarly journals An weighted ontology-based semantic similarity algorithm for web service

2009 ◽  
Vol 36 (10) ◽  
pp. 12480-12490 ◽  
Author(s):  
Min Liu ◽  
Weiming Shen ◽  
Qi Hao ◽  
Junwei Yan
Keyword(s):  
Web Service ◽  
Semantic Similarity ◽  
Similarity Algorithm

scholarly journals WordNet and Cosine Similarity based Classifier of Exam Questions using Bloom’s Taxonomy

2016 ◽  
Vol 11 (04) ◽  
pp. 142 ◽  
Author(s):  
Kithsiri Jayakodi ◽  
Madhushi Bandara ◽  
Indika Perera ◽  
Dulani Meedeniya
Keyword(s):  
Language Processing ◽  
Evaluation Process ◽  
Bloom's Taxonomy ◽  
Cosine Similarity ◽  
Stop Word ◽  
Bloom’S Taxonomy ◽  
Similarity Algorithm ◽  
Question Category ◽  
Rule Set ◽  
Derived Rules

Assessment usually plays an indispensable role in the education and it is the prime indicator of student learning achievement. Exam questions are the main form of assessment used in learning. Setting appropriate exam questions to achieve the desired outcome of the course is a challenging work for the examiner. Therefore this research is mainly focused to categorize the exam questions automatically into its learning levels using Bloom’s taxonomy. Natural Language Processing (NLP) techniques such as tokenization, stop word removal, lemmatization and tagging were used before generating the rule set to be used for this classification. WordNet similarity algorithms with NLTK and cosine similarity algorithm were developed to generate a unique set of rules to identify the question category and the weight for each exam question according to Bloom’s taxonomy. These derived rules make it easy to analyze the exam questions. Evaluators can redesign their exam papers based on the outcome of the evaluation process. A sample of examination questions of the Department of Computing and Information Systems, Wayamba University, Sri Lanka was used for the evaluation; weight assignment was done based on the total value generated from both WordNet algorithm and the cosine algorithm. Identified question categories were confirmed by a domain expert. The generated rule set indicated over 70% accuracy.

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