Content-based Filtering Methods Research Articles

Computational thinking-based informatics material recommendation system for vocational school students with the content-based filtering method

Computational thinking-based informatics material recommendation system for vocational school students with the content-based filtering method

Prototipe Sistem Rekomendasi Film Indonesia Menggunakan Pendekatan Content Based Filtering dan Metode Vector Space Model

Film represents a combination of narrative and cinematographic aspects in an audio-visual form. Films also provide an engaging visual, auditory, and emotional experience. The film industry in Indonesia has shown significant growth. In 2015, cinema audiences numbered 16.2 million, increasing to 51.2 million in 2019. The COVID-19 pandemic caused a drastic decline in the number of viewers, with only 4.5 million in 2021. However, the industry quickly recovered, reaching 54.07 million viewers in 2022 and 55 million in 2023, and is projected to reach 60 million in 2024. With the increasing availability of films and streaming platforms, finding movies that match user preferences often becomes a challenge. This study proposes a film recommendation system using the Content-Based Filtering method and the Vector Space Model to help users more easily find movies that match their preferences. Content-Based Filtering recommends films based on content similarity, while the Vector Space Model measures content similarity using metrics such as cosine similarity or euclidean distance. Based on experiments using 20 sample films, the results show that the designed system can provide accurate and precise movie recommendations based on keywords provided by users. The film "Pengabdi Setan 2: Communion" has the highest similarity score of 0.3018, followed by "Nana," "Menjelang Magrib," "Jailangkung: Sandekala," and "Qorin," with similarity scores of 0.0865, 0.0138, 0.0136, and 0.0125, respectively

IMPLEMENTATION OF THE BEST TRAINING PRODUCT SALES RECOMMENDATION SYSTEM USING COSENI SIMILARITY ALGORITHM

Introduction: Sales of training products often encounter difficulties in providing the right recommendations according to customer needs and preferences. This research aims to develop a Content-Based Filtering-based recommendation system to address this challenge, especially for PT Menara Indonesia. Method: This research focuses on the Cosine Similarity algorithm, this research designs and implements the model through Django's REST API and Streamlit's Dashboard Website. The data used consists of tabular data that includes company profiles and product models. The Content-Based Filtering method is used to improve sales efficiency and support marketing strategies. Results and Discussion: The results show that the implementation of the Cosine Similarity algorithm provides training product recommendations that are suitable and relevant to customer needs. The suitability and relevance of the recommendation results are marked by the similarity between the two items that have been combined to produce a value of 10% to 100% which has been sorted according to the recommendation results. 10% is a recommendation result that is not very similar to the input needs and 100% is the most appropriate and accurate recommendation with the input needs. Conclusion: With the use of Natural Language Processing technology, this system can produce more accurate and relevant recommendations. This research is expected to help PT Menara Indonesia in increasing sales

Marvel Movie Recommendation System Using Hybrid Item-Based and Content-Based Filtering Methods

Currently, there are so many movie genres available to the general public, making it difficult for viewers to choose a movie. One of the most popular movies is the “Marvel Movies” or MCU (Marvel Cinematic Universe), which has become the highest grossing franchise of all time with 90 movies released. The large number of movie titles makes it difficult for people to choose which movie to watch. Therefore, a Marvel movie recommendation system is needed using a hybrid item-based and content-based filtering method. The content-based method calculates the similarity between movies by identifying similar Marvel movies based on content such as genre, actor, director, and synopsis. Meanwhile, item-based completes content-based recommendations by considering user preferences. The reason for using the hybrid item-based and content-based filtering method is to be able to produce more accurate recommendations than a single method. The types and sources of data used are secondary data from journals and the internet (Imdb and Movielens), as well as datasets about Marvel movies. From the results of testing the hybrid model, the precision value is 0.8 or 80% which indicates that the model is accurate. In item-based filtering testing, the similarity result of 0.68 shows good item similarity. In the content-based filtering test, the highest similarity is 0.14 and the lowest similarity is 0.10 which shows that the similarity between the searched content and the generated content is relevant.

Movie Recommender System on Twitter Using Weighted Hybrid Filtering and GRU

The development of the industry in the film sector has experienced rapid growth, marked by the emergence of film streaming platforms such as Netflix and Disney+. With the abundance of available films, users face difficulty in choosing films that suit their preferences. Recommender systems can be a solution to this problem for users. Recommender systems rely on user reviews, making Twitter a platform that can be used to collect user reviews of a film. This study will develop a recommender system that has the potential to provide item recommendations to users using the weighted hybrid filtering and GRU methods. The weighted hybrid filtering used is a combination of collaborative filtering and content-based filtering methods. The dataset used in this study was obtained by crawling tweets relevant to the feedback of specific accounts regarding a film. The dataset resulting from the data crawling consists of a total of 854 films, 45 users and 34,086 tweets consisting of film reviews from Twitter users. The GRU model classification is performed on the results of weighted hybrid filtering with model optimization involving testing various test size scenarios and optimizer methods. The test sizes used are 40%, 30%, and 20%. The optimizer methods used include Adam, Nadam, Adamax, Adadelta, Adagrad, and SGD. The research results show that the optimal outcome is obtained using the Nadam optimization method. The performance evaluation yielded 85.74% precision, 88.63% recall, 88.63% accuracy, and 86.30% F1-score.

Content-based filtering using cosine similarity algorithm for alternative selection on training programs

The large selection of training programs provided by the Ministry of Manpower of the Republic of Indonesia makes it difficult for prospective trainees to choose a training program that suits their interests and needs. The purpose of this research is to support the selection process so that an appropriate method is needed to recommend the selection of training programs that match the interests and needs of users. One of the selection methods that can be used is the Content-Based Filtering method with similarity measurement using Cosine Similarity. The content-based filtering method is a content-based filtering method, which recommends training programs based on the suitability between the description of the training program and the interests of prospective trainees using the cosine similarity distance measurement. The test results using the Content-Based Filtering method were able to achieve an average precision value of 88%, indicating the ability of the system to provide training program recommendations that are very relevant and in accordance with the interests and needs of the trainees.

Recommendation System for Mobile Applications Tour Guide and Travel Services using Demographic Filtering and Content-Based Filtering Methods based on Android

This research aims to build a tourism service recommendation system by providing recommendations for tour guides and tourism destinations using machine learning recommendation system methods. The recommendation system method in this research uses the demographic filtering method to recommend tour guides using a model with three stages, namely filtering, scoring and sorting the tour guide data category. At the scoring stage, it is carried out using a decision-making system method using the Simple Additive Weighting method to weight the tour guide value with the weighting value of each criterion in the tour guide data. This research also applies a content-based filtering method using a model with two stages, namely training and recommendation. The training stage uses tf-idf weighting and the recommendation stage uses cosine similarity to recommend tourism spots based on the similarity of metadata for each item. In using the recommendation system, the data will focus on collecting tour guide data and data on tourist attractions selected by the user. This data will be processed by a recommendation system using demographic filtering and content-based filtering methods. Through the Simple Additive Weighting method in demographic filtering and tf-idf weighting with consistent similarity in content-based filtering, this research found personalized recommendation results so that the level of accuracy of the recommendation results becomes more personal and accurate. The results of this research provide a recommendation system for tourist guides and tourist attractions which is implemented into an Android-based mobile application that can be used to meet tourists' needs.

SISTEM REKOMENDASI FILM MENGGUNAKAN METODE CONTENT-BASED FILTERING STUDI KASUS MATERI DATA MINING DI SMK IDN BOARDING SCHOOL

Movie recommendation systems have become an interesting research topic, as they can assist users in finding movies that match their preferences. The Content-Based Filtering method is one of the methods commonly used in film recommendation systems, which focuses on film content to make recommendations. This study aims to develop a film recommendation system using the Content-Based Filtering method. This method is used to make film recommendations based on the similarity of the features of the films that have been watched by the user. This research uses case studies at SMK IDN Boarding School, where this film recommendation system was built as a means of learning data mining materials for students. Movie data is taken from the film API and processed using an algorithm to extract features from film content. Then, the system recommends films based on the similarity of features with the films that are liked by the user. The test results show that the developed film recommendation system has good performance in recommending films based on user preferences. Users can provide feedback to improve the accuracy of recommendations and the system can continue to be improved using more sophisticated methods.

Sistem Rekomendasi Jurusan SMK Menggunakan Metode Content-Based Filtering Di Kabupaten Sleman

Research background education is an important aspect for society. One level of further education is a vocational high school (SMK). There are many things that need to be considered in choosing a vocational high school, such as choosing a major based on interests, talents and abilities. In this study, the content-based filtering method was applied because it is suitable for use in cases and situations where there are more users than the system, thus allowing the system to determine the interests, talents and abilities of users based on their profile. Search for SMK majors by users (students) is more accurate, because the application can make recommendations about content that will become the core of the system when making recommendations. The purpose of this research is to build a recommendation system for junior high school students by developing a content-based filtering system that can provide major recommendations based on the students' interests, talents and abilities. It can be concluded that the department recommendation system using the weighting technique and the calculation of the Euclidean distance can provide recommendations to users according to the interests, talents and abilities of the users themselves.

Recommendation System Algorithm Content-Based Filtering Method to Provide Drink Menu Recommendations

Overchoice is a cognitive disorder in which people have difficulty making decisions when faced with many choices, that make the problem in this study. This over-choice phenomenon often occurs in choosing drinks in cafes and restaurants. This research aims to create a Recommendation System (RS) to assist in choosing the drink you want to order. Making a non-personalized hospital at the Mubtada Kopi cafe uses the best-rated and content-based filtering methods. The content-based filtering method tries to retrieve user preferences explicitly, asking the user to choose the preferences the user wants from the six content made before calculating the match between the user's preferences and the six contents in each item using the dot matrix formula. The results will be converted into a rating to match the best-rated hospital approach, which is made on a non-personalized basis. This rating matches the user's preferences and the Mubtada Kopi menu list items. The higher the rating, the better it matches the user's preferences. The order RS recommends with the Content-based filtering method is rosella tea, chocolate, lemon tea, blossom tea, and spice tea.

Sistem Rekomendasi Pemilihan Program MSIB Bagi Mahasiswa Pendidikan Informatika

This study discusses a recommendation system using a content-based filtering method with cosine similarity to help informatics education students choose the right Certified Independent Study and Internship Program (MSIB). The data used is data based on student interests, program and course data available on the MSIB web portal. The content-based filtering method is used to consider the suitability between student preferences and the MSIB program curriculum, while the cosine similarity algorithm is used to calculate the similarity score between different contents. The development of this recommendation system can assist informatics education students in choosing the MSIB program that is in accordance with the preferences of the student's interest profile. The results of the system evaluation obtained an average precision level of 89.4%, indicating that the list of recommendations provided by the system is very good and very relevant according to user preferences.

Implementation of Hybrid Method in Tourism Place Recommendation System Based on Image Features

In the industrial 4.0 era, there is an explosion of unstructured and structured data that produces broad and varied knowledge information that humans cannot process quickly. This issue makes the existence of recommendation systems meaningful. This system studies the existing information and provides suggestions according to the user's will. In the past, many recommendation systems have focused more on content-based filtering methods where recommendation results are similar based on the features of the Content that match the user's personality. This method limits the variety of information that is relevant to users. In addition, in the context of tourist attractions, many studies have not used image data that can contain many objects in one frame as a determining factor in providing recommendations. Therefore, in this study, the authors propose to add image features as one of the parameters of the recommendation system to determine the impact of using image features on the model performance. The best performance obtained is 0.364 RMSE metric using the Hybrid Image method.

Implementation of Hybrid Method in Tourism Place Recommendation System Based on Image Features

In the industrial 4.0 era, there is an explosion of unstructured and structured data that produces broad and varied knowledge information that humans cannot process quickly. This issue makes the existence of recommendation systems meaningful. This system studies the existing information and provides suggestions according to the user's will. In the past, many recommendation systems have focused more on content-based filtering methods where recommendation results are similar based on the features of the Content that match the user's personality. This method limits the variety of information that is relevant to users. In addition, in the context of tourist attractions, many studies have not used image data that can contain many objects in one frame as a determining factor in providing recommendations. Therefore, in this study, the authors propose to add image features as one of the parameters of the recommendation system to determine the impact of using image features on the model performance. The best performance obtained is 0.364 RMSE metric using the Hybrid Image method.

Mean-reversion based hybrid movie recommender system using collaborative and content-based filtering methods

Mean-reversion based hybrid movie recommender system using collaborative and content-based filtering methods

Empowerment of MSME crafts recommendation system with content-based filtering method on social commerce after the pandemic

Since the government officially declared the end of the pandemic in Indonesia, face-to-face trading activities between buyers and traders have increased again, especially in West Sumatra province. Some traders, in addition to running their businesses offline, also continue to utilize social media as they did during the pandemic, especially in marketing activities. Therefore, the purpose of this research is to assist handicraft merchants in particular, in empowering social commerce, by understanding the behavior of recommendation systems (RS) which are specialized software for filtering in social commerce. The application of the right method determines the performance of an RS. In this discussion, the method used in RS for filtering is content-based. By applying this method, it can produce an RS that is right to support the marketing techniques of handicraft shops online, by adding complete, detailed, and uniform descriptions to each promotional photo or graphic model.

Job Recommendation System Using the Content-Based Filtering Method

On the xyz application that is already running, it is a job vacancy portal. In this system there are has two users, as business accounts and job seekers. Where business accounts can post vacancies and job seekers can apply for vacancies. In this running system, when job seekers display the vacancies list, there is a list of the latest vacancies that have been posted by business people. The problem with the list of vacancies is that the list of vacancies that appear is still not in accordance with the wishes of the user as the job seeker account, thus reducing the interest in application users. There-fore, a job vacancy recommendation system is needed that can correct deficiencies in the list of vacancies that are displayed to users as job seekers. The purpose of this research to be able to pro-duce recommendations for suitable job vacancies. In this research, the authors use the content-based filtering method. Keywords: Recommendation System, Machine Learning, Content-based Filtering

An optimal context-aware content-based movie recommender system using genetic algorithm: a case study on MovieLens dataset

ABSTRACT Most research on movie recommender systems has been conducted with Collaborative Filtering (CF) methods. The lack of sufficient information about users’ interests in bootstrapping is one of the most critical problems of the CF method. Using a content-based filtering method can mitigate some of these problems. On the other hand, recent research has proven that utilising contextual information about the movie, such as genre, actors, and cast, can increase the efficiency of the recommender system. This paper uses a combined Genetic Algorithm (GA) and content-based filtering to find the best combination of genre, cast, and crew weights. We first convert each movie’s contextual information into a descriptive vector, including cast, crew, and genre. Then, we calculate the distance between each pair of sentence vectors using two separate approaches, fully connected and metadata-based. Finally, with GA, we tune the weight of each of the contextual information of movies to maximise the recommender system’s efficiency. Performance evaluation on the well-known MovieLens dataset shows that GA can improve the Precision@k criterion by producing fewer, more accurate recommendations. Weight adjustment by GA improves the F-Measure metric by approximately 58%. This, in turn, can improve Precision and Recall metrics. Also, the GA offers a higher correct recommendation rate than other methods.

Sistem Rekomendasi Event Online Menggunakan Metode Content Based Filtering

Covid-19 has spread widely with various variants, disrupting human social activities, the activities in question include the implementation of events, but there are other alternatives by utilizing technology, events can still be held online. However, the number of events that are held online makes it difficult for someone to find the event they want because there are too many events on the website so someone experiences information overload. Therefore, the authors conduct research with the goal to make it easier for someone to find the event they want by implementing a recommendation system on the website. In this study, the authors make a recommendation system using the Content-Based Filtering method in recommending an online event. The results of this study obtained an accuracy of 86% of the performance of the model carried out by SVM.

Hotel Recommendation System with Content-Based Filtering Approach (Case Study: Hotel in Yogyakarta on Nusatrip Website)

The increasing of Covid-19 pandemic has hampered people's activities, often causing stress if they are only stay at home continuously. This has led to an increasing trend of staycations or holiday activities in the city itself by renting a hotel. Hotel rental technology has begun to be transferred with the existence of OTA (Online Travel Agent). The existence of various kinds of hotels with various kinds of facilities that makes people feel confused in choosing which hotel to occupy. To help overcome this, the researchers tried to create a recommendation system to help prospective hotel residents choose the hotel according to their choice. In addition, it can also assist companies in increasing hotel room reservations through its website. In this study, researchers will build a hotel recommendation system in Yogyakarta at one of the OTAs in Indonesia using Content-Based Filtering Methods, weighting text data using Term Frequency-Inverse Document Frequency (TF-IDF) Methods and measuring document similarity using Cosine Similarity Methods. Based on the results of the Good Karma Yogyakarta hotel recommendations as a test example, 10 similar hotels were obtained, namely Happy Buddha Yogyakarta – Hostel, Nextdoor Homestay, Hotel Puspita, OYO 426 Hotel Gading Resto, Omah Jegog Homestay, Prawirotaman Homestay, RedDoorz near Prawirotaman, Ayodhya Garden Hostel Yogyakarta by HOM, Bringin House Yogyakarta, and House 24 Yogyakarta with cosine similarity values 0.956666513, 0.946570717, 0.917459394, 0.912534877, 0.886439718, 0.88221982, 0.881775275, 0.875845794, 0.872030219, and 0.871514859.

Recommendation System Model for Political Communication through Social Media using Content Based Filtering Method

Recommendation System Model for Political Communication through Social Media using Content Based Filtering Method