A Movie recommender system based on topic modeling using machine learning methods

Kordabadi, Mojtaba; Nazari, Amin; Mansoorizadeh, Muharram

doi:10.21203/rs.3.rs-1869013/v1

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…On the other hand, the focus of recommendation systems is to help users find products and services based on their interests and preferences. The distinction is important since the former is a much newer field of study while the latter has rich and active literature [15,16].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Suggestion Detection in Online User Reviews through Integrated Information Retrieval and Deep Learning Approaches

Hadizadeh,

Nazari,

Mansoorizadeh

2024

JWE

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In the aftermath of the COVID-19 pandemic, using web platforms as a communication medium and decision-making tool in online commerce has become widely acknowledged. User-generated comments, reflecting positive and negative sentiments towards specific items, serve as invaluable indicators, offering recommendations for product and organizational improvements. Consequently, the extraction of suggestions from mined opinions can enhance the efficacy of companies and organizations in this domain. Prevailing research in suggestion mining predominantly employs rule-based methodologies and statistical classifiers, relying on manually identified features. However, a recent trend has emerged wherein researchers explore solutions grounded in deep learning tools and techniques. This study aims to employ information retrieval techniques for the automated identification of suggestions. To this end, various methodologies, including distance measurement approaches, multilayer perceptron neural networks, support vector machines, regression logistics, convolutional neural networks utilizing TF-IDF, Bag of Words (BOW), and Word2Vec vectors, along with keyword extraction, have been integrated. The proposed approach is assessed using the SemEval2019 dataset to extract suggestions from the textual content of online user reviews. The obtained results demonstrate a notable enhancement in the F1 score, reaching 0.76 compared to prior research. The experiments further suggest that information retrieval-based approaches exhibit promising potential for this specific task.

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Section: Introductionmentioning

confidence: 99%

Enhancing Suggestion Detection in Online User Reviews through Integrated Information Retrieval and Deep Learning Approaches

Hadizadeh,

Nazari,

Mansoorizadeh

2024

JWE

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show abstract

“…Suggestion mining can employ in various applications such as product improvement ideas, customer-to-customer suggestions, suggestion summarization, sentiment recognition, and recommendation systems [10,11]. Recommender systems are also systems that help users to find desired items [12,13].…”

Section: Introductionmentioning

confidence: 99%

Automatic Suggestion Detection from Online User Reviews with Information Retrieval and Deep Learning

Hadizadeh,

Nazari,

Mansoorizadeh

2023

Preprint

Self Cite

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After the outbreak of COVID-19, the use of the web platform as a means of communication between users and decision-making in online shopping has been accepted in e-commerce. Comments of users express substantially positive and negative emotions about a particular item, which offers suggestions for the improvement of products and organizations. Therefore, mining opinions to extract suggestions can increase the utility of companies and organizations. A good deal of research on suggestion mining employs rule-based approaches and statistical classifiers using manually identified features, while recently, several researchers have considered solutions based on deep learning tools and techniques. The purpose of this study is to use information retrieval techniques for the automatic detection of suggestions. Therefore, distance measurement approaches, multilayer perceptron neural networks, support vector machines, regression logistics, convolutional neural networks with TF-IDF, BOW, and Word2Vec vectors, and keyword extraction were adopted. The proposed approach is presented on the SemEval2019 dataset for extracting suggestions from the text of online user reviews. The results show that the F1Score has improved, as compared to the previous work, and has reached 0.87. The experiments indicate that information retrieval-based approaches tend to be promising for this task.

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Text recuperated using ontology with stable marriage optimization technique and text visualization using AR

Ouali,

Ben Halima,

Masmoudi

et al. 2024

Multimed Tools Appl

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A Movie recommender system based on topic modeling using machine learning methods

Cited by 3 publications

References 25 publications

Enhancing Suggestion Detection in Online User Reviews through Integrated Information Retrieval and Deep Learning Approaches

Enhancing Suggestion Detection in Online User Reviews through Integrated Information Retrieval and Deep Learning Approaches

Automatic Suggestion Detection from Online User Reviews with Information Retrieval and Deep Learning

Text recuperated using ontology with stable marriage optimization technique and text visualization using AR

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