Movie Recommender system using Sentiment Analysis

Chauhan, Anmol; Nagar, Deepank; Chaudhary, Prashant

doi:10.1109/iciptm52218.2021.9388340

Cited by 14 publications

(4 citation statements)

References 10 publications

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“…Caching is the act of keeping a copy of user-relevant data on the client side and using that instead of reading from the database every time an operation should occur. By doing it this way, there is no need for performing a read every single time to the backend and instead only doing so when a change is directed via listeners [9]. The actual execution of this caching behavior can be done through runtime-specific tools such as singletons to keep information in memory or through local save data that can persist between sessions.…”

Section: Efficient Read/write Structurementioning

confidence: 99%

A Data-Driven Application for Matching Student Traits with Learning Opportunities using Artificial Intelligence

You¹,

Park²

2023

Artificial Intelligence Advances

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It is often limited to students the opportunities they can get to apply their knowledge and learn new things, as different opportunities have vastly different ways of advertising. Just like the students, many organizations are looking for passionate students to apply their knowledge and energy to benefit society [2]. To solve this problem and ease the difficulties in finding the right opportunities, Maclever aims to be a place where all organizations can post their opportunities for students, and students can use the artificial intelligence-based feature in this application to find opportunities that best fit their skills. Maclever aims to be a simple and effective connection between organizations and students [3]. Leveraging tools such as sentiment analysis and utilizing models based on user behaviors and preferences to better match valuable connections allows us to create a system that gives us a much stronger ability to address the goals Maclevers sets out to solve.

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Section: Efficient Read/write Structurementioning

confidence: 99%

A Data-Driven Application for Matching Student Traits with Learning Opportunities using Artificial Intelligence

You¹,

Park²

2023

Artificial Intelligence Advances

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“…The many movies produced make it confusing for users to choose the movie they want. Therefore, a recommendation system is formed that can be used by users to utilize their previous preferences in selecting and recommending items efficiently [3].…”

Section: Introductionmentioning

confidence: 99%

Film Recommendation System Using Content-Based Filtering and the Convolutional Neural Network (CNN) Classification Methods

Nilla,

Setiawan

2024

J. Ilm. Tek. Elektro Komput. Dan Inform

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Managing large amounts of data is a challenge faced by users, so a recommendation system is needed as an information filter to provide relevant item suggestions. Twitter is often used to find information about movie reviews that can be used a basis for developing recommendation systems. This research contributes to applying content-based filtering in the context of Convolutional Neural Network (CNN). To the best of the researcher's knowledge, there has been no research addressing this combination of method and classification. The main focus is to evaluate the development of a recommendation system by integrating and comparing similarity identification methods using the RoBERTa and TF-IDF approaches. In this research, Roberta and TF-IDF as vectorizer and classification methods are applied to form a model that can recognize patterns in data and produce accurate predictions based on its features. The total data used is 854 movies and 34086 film reviews from 44 Twitter accounts. The SMOTE method was applied as a technique to overcome data imbalance. The research was conducted three times with increasing accuracy results. The first experiment TF-IDF as baseline, SMOTE on CNN classification. The second experiment, applying baseline, SMOTE, embedding on CNN classification. The third experiment applied baseline, SMOTE, embedding, and optimizer to CNN classification. The experimental results show that TF-IDF as baseline, SMOTE, embedding and SGD optimizer with the best learning rate on CNN classification can provide optimal results with an accuracy rate of 86.41%. Thus, the system can provide relevant movie recommendations with good prediction accuracy and performance.

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“…The recommender systems are tools that provide suggestions for their users according to the users' need [1,2]. The recommender systems can help facilitate people's lives in terms of many aspects, e.g., product recommendation [3][4][5][6], movie recommendation [7][8][9], travel recommendation [10,11], knowledge recommendation [12,13], and research paper recommendation [14,15].…”

Section: Introductionmentioning

confidence: 99%

An Online Course Recommender System in e-Learning Using Learners’ Profile and Learning Behavior-Based Mechanism

Charoensuk

2024

IJIET

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The advent of online learning platforms, such as Coursera and Udemy, has facilitated global access to diverse educational opportunities. However, integrating recommender algorithms into these online learning systems presents a notable challenge. To address this challenge, this research paper proposes an architecture for online course recommender systems that leverages learners’ profiles and learning behavior. The proposed architecture comprises six core components: 1) User Management Engine, 2) Online Course Content Management Engine, 3) Learning Behavior Observer and Recorder, 4) Profile and Learning Behavior Builder Engine, 5) Course Recommendation and Filtering Engine, and 6) Feedback Engine. These components collectively form the foundation of the online course recommender system. In order to facilitate practical implementation, a prototype of the online course recommender system has been developed. To evaluate the system’s performance, a comparative analysis was conducted, to compare the personalized recommended list with the non-personalized recommended list. The analysis revealed an average precision of 77.60% for the personalized recommended list, while the non-personalized recommended list achieved an average precision of 65.60%. These findings highlight the superiority of the personalized approach in generating more accurate and relevant online course recommendations.

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Movie Recommender system using Sentiment Analysis

Cited by 14 publications

References 10 publications

A Data-Driven Application for Matching Student Traits with Learning Opportunities using Artificial Intelligence

A Data-Driven Application for Matching Student Traits with Learning Opportunities using Artificial Intelligence

Film Recommendation System Using Content-Based Filtering and the Convolutional Neural Network (CNN) Classification Methods

An Online Course Recommender System in e-Learning Using Learners’ Profile and Learning Behavior-Based Mechanism

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