scholarly journals Generating Quality Items Recommendation by Fusing Content based and Collaborative filtering

2019 ◽  
Vol 8 (9S) ◽  
pp. 494-498
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Traditional Approach ◽  
Hybrid Approach ◽  
Relevant Information ◽  
News Article ◽  
Comparison Results ◽  
Evaluation Measure ◽  
Content Based Filtering

Recommendation system has become an inevitable part of our life. It has already spread its prominence in various fields like movies, music, news, article recommendations etc. Due to the influence of social media, data is streaming from all over the Internet. Collect the relevant information from chunks of data available has become much difficult. Recommender systems guides in filtering data to get the relevant information. Commonly used recommendation approaches are content based filtering and collaborative filtering. Each approach has its own limitations. The hybrid approach combines the advantages of both the approaches. In this paper, we have tried to enhance the quality of the items recommendation system by fusing both content based and collaborative filtering uniquely. The experimental results are compared with that of other traditional approach using precision and recall evaluation measure. The comparison results show that our approach has 10% better precision for top-10 recommendations than other established recommendation technique.

scholarly journals Hybrid Filtering Recommendation System in an Educational Context

2022 ◽  
Vol 17 (1) ◽  
pp. 0-0
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Educational Context ◽  
Hybrid Filtering ◽  
Content Based Filtering ◽  
Research Studies

In education, the needs of learners are different in the majority of the time, as each has specificities in terms of preferences, performance and goals. Recommendation systems have proven to be an effective way to ensure this learning personalization. Already used and tested in other areas such as e-commerce, their adaptation to the educational context has led to several research studies that have tried to find the best approaches with the best expected results. This article suggests that a hybridization of recommendation systems filtering methods can improve the quality of recommendations. An experiment was conducted to test an approach that combines content-based filtering and collaborative filtering. The results proved to be convincing.

scholarly journals Recommendation System: Techniques and Issues

2019 ◽  
Vol 8 (3) ◽  
pp. 2821-2824
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Information Overload ◽  
Daily Life ◽  
Hybrid Approach ◽  
The Internet ◽  
Individual Interest ◽  
The Many ◽  
Content Based Filtering

In daily life user searched the many things over the internet on the basis of requirement with the help of search engines. Recommendation systems are widely used on the internet to help the user in discover the products or services that are best with their individual interest. RS effectively reduce the information overload by providing personalized suggestions to user when searching for items like movies, songs, or books etc. The main aim of RS is to help the users by providing the surface of information that relevant to them, fulfill their needs and their task. The paper provides an overview of RS and analyze the different approaches used for develop RS that include collaborative filtering, content-based filtering and hybrid approach of recommender system.

scholarly journals Song Recommendation System Using Maximal b-Matching

2015 ◽  
Vol 4 (3) ◽  
pp. 109
Author(s):  
Deepa S ◽  
Varsha R ◽  
Parvathi R
Keyword(s):  
Social Media ◽  
Collaborative Filtering ◽  
Paradigm Shift ◽  
Relative Frequency ◽  
Recommendation System ◽  
Relevant Information ◽  
Capacity Constraints ◽  
Social Content ◽  
Media Applications ◽  
Content Based Filtering

The last decade has witnessed a fundamental paradigm shift on how information content is distributed among people. Nowadays, an increasing number of platforms allow everyone to participate both in information production and information consumption. The phenomenon has been coined as democratization of content. However, as the opportunities to find relevant information and relevant audience increases, so does the complexity of a system that would allow suppliers and consumers to meet in the most efficient way. Our motivation is building a “featured item” component for social-media applications. Such a component would provide recommendations to consumers each time they login the system. The existing system follows either collaborative filtering or content based filtering. Collaborative filtering methods are based on collecting and analyzing a large amount of information on user’s behaviours, activities or preferences and predicting what users will like based on their similarity to other users. Content-based filtering methods are based on a description of the item and a profile of the user's preference. Both of these methods require input from the user in the form of ratings or other user's likes. But social content matching takes into account only the user's preferences and also the capacity constraints. For each item 't' and each user 'u', consider constraints on the maximum number of edges that t and u can participate in the matching. These capacity constraints can be estimated by the activity of each user and the relative frequency with which items need to be delivered. Here we introduce the concept called b-matching goal is to find a matching that satisfies all capacity constraints and maximizes the total weight of the edges in the matching. The result of b-matching is the set of songs that are to be recommended to the user based on his likes.

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

User opinions driven social recommendation system

2021 ◽  
Vol 25 (1) ◽  
pp. 21-31
Author(s):  
Lakshmikanth Paleti ◽  
P. Radha Krishna ◽  
J.V.R. Murthy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Social Recommendation ◽  
Novel Approach ◽  
Tripartite Graphs ◽  
Content Based Filtering

Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.

scholarly journals Hotel Recommender System based on Knowledge Graph and Collaborative Approach

2021 ◽  
pp. 63-71
Author(s):  
Yousef Abuzir ◽  
Mohamed Dwieb
Keyword(s):  
Recommendation System ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Knowledge Graph ◽  
Similarity Function ◽  
Collaborative Approach ◽  
Analysis Techniques ◽  
Business Sectors ◽  
F Measure

With the rapid increase of Information technology, online services and social media, recommendation system becomes an important issue and a need for both the customer and business sectors. The main aim of traditional and online recommendation systems is to recommend the desired and the necessary services that are appropriate recommendations to users. Traditional recommendation systems often suffer from inefficient data analysis techniques, rating the different services without regard to the previous preferences of the users and do not meet the personal demands of the users. Therefore, in this paper we used a hybrid approach based on Knowledge graph and Machine Learning similarity function as a recommendation system. We used real datasets to conduct the experiment. We built the knowledge graph for the visitors, hotels and their ranks, and we used the knowledge graph and similarity scores to recommend a hotel or a set of hotels for the visitors based on former preferences and ratings of other visitors. The results show significant accuracy and good quality of service recommender systems with 93.5% for f-measure.

scholarly journals Penerapan Content-Boosted Collaborative Filtering untuk Meningkatkan Kemampuan Sistem Rekomendasi Penyedia Jasa Acara Pernikahan

2020 ◽  
Vol 12 (1) ◽  
pp. 112
Author(s):  
Rahman Indra Kesuma ◽  
Amirul Iqbal
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Digital World ◽  
Business Opportunities ◽  
Efficiency And Effectiveness ◽  
Long Time ◽  
History Of ◽  
Smartphone Technology ◽  
World Development

AbstractThe changes in lifestyle of the global society in the era of digital world development have made the smartphone technology penetration to rise continually. This condition can increase business opportunities, especially e-commerce activities that utilize technology and the internet in terms of promotions and transactions. The efficiency and effectiveness is an interesting focus that is discussed in this issue. For example, in services or products searching for a wedding where many customers still feel difficult and need a long time to find the desired things. The existence of a recommendation system also has not been able to help, especially for users who are newly registered to the system. This is because most of them will provide recommendations based on a history of user activity. Therefore, this study applies the content-boosted collaborative filtering (CBCF) method to improve the ability of the recommendation system in providing recommendations for weddings, especially for a new user. The obtained results are then compared with two commonly used methods, content-based recommendations (CB) and collaborative filtering (CF). Based on the experimental results, it can be concluded that CBCF can maintain the quality of good recommendations for long registered users with an accuracy of 84% and also can provide recommendations for new users with an accuracy of 54% which is cannot be solved by CB or CF methods.Key Word: digital businesses, wedding vendors/organizers, recommendation system, content-boosted collaborative filtering  AbstrakPerubahan pola kehidupan masyarakat global pada era perkembangan dunia digital membuat penetrasi dari teknologi telepon pintar terus menaik. Kondisi ini dapat meningkatkan kesempatan bisnis khususnya kegiatan jual beli yang memanfaatkan teknologi dan internet dalam hal promosi dan transaksi. Efisiensi dan efektifitas proses menjadi fokus yang terus menarik dibahas dalam hal ini. Sebagai contoh, pada pencarian layanan atau produk untuk pernikahan yang mana banyak pelanggan masih merasakan kesulitan dan membutuhkan waktu yang lama untuk mencari sesuatu yang diinginkannya. Keberadaan sistem rekomendasi juga belum bisa membantu terlebih bagi pengguna yang baru terdaftar pada sistem. Hal ini dikarenakan kebanyakan sistem akan memberikan rekomendasi berdasarkan rekam jejak aktifitas pengguna. Maka itu, pada penelitian ini diusulkan penerapan metode content-boosted collaborative filtering (CBCF) untuk meningkatkan kemampuan sistem rekomendasi dalam pemberian rekomendasi untuk acara pernikahan, khususnya pada pengguna baru. Hasil yang diperoleh selanjutnya dibandingkan dengan dua metode yang umum digunakan yaitu content based recommendation (CB) dan collaborative filtering (CF). Berdasarkan hasil penelitian yang diperoleh, dapat disimpulkan bahwa CBCF dapat mempertahankan kualitas pemberian rekomendasi yang baik untuk pengguna lama dengan akurasi sebesar 84% serta mampu memberikan rekomendasi untuk pengguna baru dengan akurasi 54% yang mana kondisi ini tidak bisa diselesaikan oleh metode CB ataupun CF.Kata Kunci: bisnis digital, penyedia jasa acara pernikahan, sistem rekomendasi, content-boosted collaborative filtering 

scholarly journals Movie Recommendation System using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 4778-4781
Author(s):  
Ghanashyam Vibhandik
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Cosine Similarity ◽  
Daily Lives ◽  
Movie Content ◽  
Movie Recommendation ◽  
The Many ◽  
The Individual ◽  
Content Based Filtering ◽  
Filtering Approach

Movies are very significant in our lives. It is one of the many forms of entertainment that we encounter in our daily lives. It is up to the individual to decide whatever type of film they choose to see, whether it is a comedy, romantic film, action film, or adventure film. However, the issue is locating acceptable content, as there is a large amount of information created each year. As a result, finding our favourite film is really difficult. The goal of this research is to improve the regular filtering technique's performance and accuracy. A recommendation system can be implemented using a variety of approaches. Content-based filtering and collaborative filtering strategies are employed in this work. The content-based filtering approach analyses the user's history/past behaviour and recommends a list of comparable movies depending on their input. K-NN algorithms and collaborative filtering are also employed in this paper to improve the accuracy of the results. Cosine similarity is utilised in this work to quickly discover comparable information. The correctness of the cosine angle is measured by cosine similarity. People may quickly find their favourite movie content thanks to all of this.

scholarly journals Personalized Suggestion For Music Based On Collaborative Filtering

2020 ◽  
Vol 9 (05) ◽  
pp. 25047-25051
Author(s):  
Aniket Salunke ◽  
Ruchika Kukreja ◽  
Jayesh Kharche ◽  
Amit Nerurkar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Cold Start ◽  
The Internet ◽  
Music Recommendation ◽  
User Similarity ◽  
Content Based Filtering ◽  
Music Recommendation System ◽  
Cold Start Problem

With the advancement of technology there are millions of songs available on the internet and this creates problem for a person to choose from this vast pool of songs. So, there should be some middleman who must do this task on behalf of user and present most relevant songs that perfectly fits the user’s taste. This task is done by recommendation system. Music recommendation system predicts the user liking towards a particular song based on the listening history and profile. Most of the music recommendation system available today will give most recently played song or songs which have overall highest rating as suggestions to users but these suggestions are not personalized. The paper purposes how the recommendation systems can be used to give personalized suggestions to each and every user with the help of collaborative filtering which uses user similarity to give suggestions. The paper aims at implementing this idea and solving the cold start problem using content based filtering at the start.

Sign in / Sign up

Export Citation Format

Share Document