Cosine similarity-based algorithm for social networking recommendation

Al-Otaibi, Shaha; Altwoijry, Nourah; Alqahtani, Alanoud; Aldheem, Latifah; Alqhatani, Mohrah; Alsuraiby, Nouf; Alsaif, Sarah; Albarrak, Shahla

doi:10.11591/ijece.v12i2.pp1881-1892

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…TF-IDF is a mathematical algorithm used to calculate the weight of significant words in a text [28], [29] on the assumption that a word appearing frequently in the text is usually with high term frequency (TF), while a low document frequency (DF) can be found when a word does not appear in any other texts, but with a high inverse document frequency (IDF) as seen in ( 1) for word weight calculation [30], [31].…”

Section: Term Frequency-inverse Document Frequencymentioning

confidence: 99%

A Thai-language chatbot analyzing mosquito-borne diseases using Jaccard similarity

Chanakot,

Sanrach

2024

Bulletin EEI

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The objective of this study is to create a Thai-language chatbot analyzing mosquito-borne diseases using Jaccard similarity with an aim to develop an artificial intelligence (AI)-based chatbot used to analyze Aedes-borne diseases through natural language processing. The analysis occurred when the symptoms provided by users on the chatbot were assessed to select relevant words as text attributes using the term frequency-inverse document frequency (TF-IDF) before the Jaccard similarity was used to measure the similarity of the information on the mosquito-borne disease database. The Line Messaging API was applied to facilitate communication between users and the chatbot through the Line application. The chatbot applied PHP 7.2.34 and MySQL 5.7.32 for database management, with Apache 2.2.29 serving as the bot server. The performance evaluation of the chatbot revealed that the chatbot accurately understood user intentions with an intent accuracy of 85.00%. Likewise, the usability of the chatbot was assessed using the system usability scale (SUS), and it received a score of 89.75, indicating a high level of user-friendliness. Furthermore, it has been found that appropriate tokenization enables accurate feature selection. This leads to improved accuracy in measuring Jaccard similarity. Consequently, the chatbot is capable of providing precise responses that align with the user's intent.

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Section: Term Frequency-inverse Document Frequencymentioning

confidence: 99%

A Thai-language chatbot analyzing mosquito-borne diseases using Jaccard similarity

Chanakot,

Sanrach

2024

Bulletin EEI

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“…Cosine similarity is a method to measure the level of similarity between two vectors, which in this case is every feature in the data. The Cosine Similarity will decide whether the size of the similarity between the two vectors has a cosine angle in the same direction or not [22].…”

Section: Cosine Similaritymentioning

confidence: 99%

Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm

Saleh

Laia

Gowasa

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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One of the neural network algorithms that can be used in iris recognition is self-organizing map (SOM). This algorithm has a weakness in determining the initial weight of the network, which is generally carried out randomly, which can result in a decrease in accuracy when an incorrect determination is made. The solution that is often used is to apply a hybrid process in determining the initial weight of the SOM network. This study takes an approach using the cosine similarity equation to determine the initial weight of the network SOM in order to increase recognition accuracy. In addition, the localization process needs to be carried out to limit the area of the iris image being studied so that it is easy for the recognition process to be carried out. The method proposed in this study for iris recognition, namely hybrid SOM and Daugman’s algorithm, has been tested on several people by capturing the iris of the eye using a digital camera. The captured eyes have been localized first using the Daugman’s algorithm, and then the image features were extracted using the GLCM and LBP methods. In the final stage of the study, an iris recognition comparison test was performed, and the results obtained an accuracy of 85.50% using the proposed method and an accuracy of 73.50% without performing a hybrid process on the SOM network.

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“…The K-means recommendation system [20] was developed by utilizing customer personal data such as age and gender to generate clustered customer profiles using the K-means method [21]. Each cluster where the customers live is analyzed using collaborative filtering to generate movie recommendations that fit into each cluster.…”

Section: Introductionmentioning

confidence: 99%

A hybrid recommender system based on customer behavior and transaction data using generalized sequential pattern algorithm

2022

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In the future, the quality of product suggestions in online retailers will influence client purchasing decisions. Unqualified product suggestions can result in two sorts of errors: false negatives and false positives. Customers may not return to the online store as a result of this. By merging sales transaction data and consumer behavior data in clickstream data format, this work offers a hybrid recommender system in an online store utilizing sequential pattern mining (SPM). Based on the clickstream data components, the product data whose status is only observed by consumers is assessed using the simple additive weighting (SAW) approach. Products with the two highest-ranking values are then coupled with product data that has been purchased and examined in the SPM using the generalized sequential pattern (GSP) method. The GSP algorithm produces rules in a sequence pattern, which are then utilized to construct product suggestions. According to the test results, product suggestions derived from a mix of sales transaction data and consumer behavior data outperform product recommendations generated just from sales transaction data. Precision, recall, and F-measure metrics values rose by 185.46, 170.83, and 178.43%, respectively.

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Cosine similarity-based algorithm for social networking recommendation

Cited by 7 publications

References 28 publications

A Thai-language chatbot analyzing mosquito-borne diseases using Jaccard similarity

A Thai-language chatbot analyzing mosquito-borne diseases using Jaccard similarity

Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm

A hybrid recommender system based on customer behavior and transaction data using generalized sequential pattern algorithm

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