An Effective Recommender System Based on Clustering Technique for TED Talks

2020 ◽  
Vol 15 (1) ◽  
pp. 35-51 ◽  
Author(s):  
Faiz Maazouzi ◽  
Hafed Zarzour ◽  
Yaser Jararweh
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
State Of The Art ◽  
Pearson Correlation ◽  
Enormous Amount ◽  
Powerful Mechanism ◽  
Ted Talks

With the enormous amount of information circulating on the Web, it is becoming increasingly difficult to find the necessary and useful information quickly and efficiently. However, with the emergence of recommender systems in the 1990s, reducing information overload became easy. In the last few years, many recommender systems employ the collaborative filtering technology, which has been proven to be one of the most successful techniques in recommender systems. Nowadays, the latest generation of collaborative filtering methods still requires further improvements to make the recommendations more efficient and accurate. Therefore, the objective of this article is to propose a new effective recommender system for TED talks that first groups users according to their preferences, and then provides a powerful mechanism to improve the quality of recommendations for users. In this context, the authors used the Pearson Correlation Coefficient (PCC) method and TED talks to create the TED user-user matrix. Then, they used the k-means clustering method to group the same users in clusters and create a predictive model. Finally, they used this model to make relevant recommendations to other users. The experimental results on real dataset show that their approach significantly outperforms the state-of-the-art methods in terms of RMSE, precision, recall, and F1 scores.

NINU: An Incremental User-Based Algorithm for Data Sparsity Recommender Systems

2012 ◽  
Vol 201-202 ◽  
pp. 428-432
Author(s):  
Yang Zhang ◽  
Hua Shen ◽  
Guo Shun Zhou
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Web Sites ◽  
Rapid Growth ◽  
Information Overload ◽  
Pearson Correlation ◽  
Sparse Data ◽  
Data Set ◽  
Online Prediction

Collaborative Filtering (CF) algorithms are widely used in recommender systems to deal with information overload. However, with the rapid growth in the amount of information and the number of visitors to web sites in recent years, CF researchers are facing challenges with improving the quality of recommendations for users with sparse data and improving the scalability of the CF algorithms. To address these issues, an incremental user-based algorithm combined with item-based approach is proposed in this paper. By using N-nearest users and N-nearest items in the prediction generation, the algorithm requires an O(N) space for storing necessary similarities for the online prediction computation and at the same time gets improvement of scalability. The experiments suggest that the incremental user-based algorithm provides better quality than the best available classic Pearson correlation-based CF algorithms when the data set is sparse.

scholarly journals A New Similarity Measure for User-based Collaborative Filtering in Recommender Systems

2015 ◽  
Vol 14 (9) ◽  
pp. 6118-6128 ◽  
Author(s):  
T. Srikanth ◽  
M. Shashi
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
Distance Measure ◽  
Current User ◽  
Challenging Problem ◽  
Popular Approach ◽  
Similarity Calculation

Collaborative filtering is a popular approach in recommender Systems that helps users in identifying the items they may like in a wagon of items. Finding similarity among users with the available item ratings so as to predict rating(s) for unseen item(s) based on the preferences of likeminded users for the current user is a challenging problem. Traditional measures like Cosine similarity and Pearson correlation’s correlation exhibit some drawbacks in similarity calculation. This paper presents a new similarity measure which improves the performance of Recommender System. Experimental results on MovieLens dataset show that our proposed distance measure improves the quality of prediction. We present clustering results as an extension to validate the effectiveness of our proposed method.

scholarly journals Syn Rec: A Prediction Technique using Collaborative Filtering and Synergy Score

2019 ◽  
Vol 8 (5S3) ◽  
pp. 457-463
Keyword(s):  
Social Interactions ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Effective Technique ◽  
Novel Technique ◽  
Trust Relation ◽  
Prediction Technique ◽  
User Trust

Recommender systems are techniques designed to produce personalized recommendations. Data sparsity, scalability cold start and quality of prediction are some of the problems faced by a recommender system. Traditional recommender systems consider that all the users are independent and identical, its an assumption which leads to a total ignorance of social interactions and trust among user. Trust relation among users ease the work of recommender systems to produce better quality of recommendations. In this paper, an effective technique is proposed using trust factor extracted with help of ratings given so that quality can be improved and better predictions can be done. A novel-technique has been proposed for recommender system using film-trust dataset and its effectiveness has been justified with the help of experiments.

scholarly journals Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering

2021 ◽  
Vol 11 (24) ◽  
pp. 11890
Author(s):  
Silvana Vanesa Aciar ◽  
Ramón Fabregat ◽  
Teodor Jové ◽  
Gabriela Aciar
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
User Experiences ◽  
Relevant Feedback ◽  
Filtering Method

Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to provide recommender systems with methods that add information about the experiences of other users, along with the presentation of the recommended products. These methods should help users by filtering reviews and presenting the necessary answers to their questions about recommended products. The contribution of this work is the description of a recommender system that recommends products using a collaborative filtering method, and which adds only relevant feedback from other users about recommended products. A prototype of a hotel recommender system was implemented and validated with real users.

State of the Art Recommendation Approaches

2020 ◽  
pp. 1621-1651
Author(s):  
Bhupesh Rawat ◽  
Sanjay K. Dwivedi
Keyword(s):  
Recommender Systems ◽  
Domain Knowledge ◽  
Information Overload ◽  
State Of The Art ◽  
New Techniques ◽  
Contextual Data ◽  
Additional Information ◽  
E Learning ◽  
Traditional Approaches

Recommender systems have been used successfully in order to deal with information overload problems in a wide variety of domains ranging from e-commerce, e-tourism, to e-learning. They typically predict the ratings of unseen items by a user and recommend the top N items based on user's profile. Moreover, the profile can be enriched further by using additional information such as contextual data, domain knowledge, and tagging information among others for improving the quality of recommendations. Traditional approaches have not been effective in exploiting these additional data sources. Hence, new techniques need to be developed for extracting and integrating them into the recommendation process. In this article, the authors present a survey on state of the art recommendation approaches their algorithms, issues and also provides further research directions for developing smart and intelligent recommender systems.

scholarly journals Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities

2020 ◽  
Vol 10 (21) ◽  
pp. 7748
Author(s):  
Zeshan Fayyaz ◽  
Mahsa Ebrahimian ◽  
Dina Nawara ◽  
Ahmed Ibrahim ◽  
Rasha Kashef
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Information Overload ◽  
State Of The Art ◽  
Recommendation Systems ◽  
Qualitative Evaluation ◽  
Potential Influence ◽  
Systems Research ◽  
Current State

Recommender systems are widely used to provide users with recommendations based on their preferences. With the ever-growing volume of information online, recommender systems have been a useful tool to overcome information overload. The utilization of recommender systems cannot be overstated, given its potential influence to ameliorate many over-choice challenges. There are many types of recommendation systems with different methodologies and concepts. Various applications have adopted recommendation systems, including e-commerce, healthcare, transportation, agriculture, and media. This paper provides the current landscape of recommender systems research and identifies directions in the field in various applications. This article provides an overview of the current state of the art in recommendation systems, their types, challenges, limitations, and business adoptions. To assess the quality of a recommendation system, qualitative evaluation metrics are discussed in the paper.

Hybrid Recommender System Using Emotional Fingerprints Model

2019 ◽  
Vol 9 (3) ◽  
pp. 48-70
Author(s):  
Anthony Nosshi ◽  
Aziza Saad Asem ◽  
Mohammed Badr Senousy
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Experimental Simulation ◽  
Key Factors ◽  
Easy Task ◽  
Needed Information ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

With today's information overload, recommender systems are important to help users in finding needed information. In the movies domain, finding a good movie to watch is not an easy task. Emotions play an important role in deciding which movie to watch. People usually express their emotions in reviews or comments about the movies. In this article, an emotional fingerprint-based model (EFBM) for movies recommendation is proposed. The model is based on grouping movies by emotional patterns of some key factors changing in time and forming fingerprints or emotional tracks, which are the heart of the proposed recommender. Then, it is incorporated into collaborative filtering to detect the interest connected with topics. Experimental simulation is conducted to understand the behavior of the proposed approach. Results are represented to evaluate the proposed recommender.

State of the Art Recommendation Approaches

2018 ◽  
Vol 10 (1) ◽  
pp. 51-76
Author(s):  
Bhupesh Rawat ◽  
Sanjay K. Dwivedi
Keyword(s):  
Recommender Systems ◽  
Domain Knowledge ◽  
Information Overload ◽  
State Of The Art ◽  
New Techniques ◽  
Contextual Data ◽  
Additional Information ◽  
E Learning ◽  
Traditional Approaches

Recommender systems have been used successfully in order to deal with information overload problems in a wide variety of domains ranging from e-commerce, e-tourism, to e-learning. They typically predict the ratings of unseen items by a user and recommend the top N items based on user's profile. Moreover, the profile can be enriched further by using additional information such as contextual data, domain knowledge, and tagging information among others for improving the quality of recommendations. Traditional approaches have not been effective in exploiting these additional data sources. Hence, new techniques need to be developed for extracting and integrating them into the recommendation process. In this article, the authors present a survey on state of the art recommendation approaches their algorithms, issues and also provides further research directions for developing smart and intelligent recommender systems.

scholarly journals A Research on Collaborative Filtering Based Movie Recommendations: From Neighborhood to Deep Learning Based System

2019 ◽  
Vol 8 (4) ◽  
pp. 10809-10814
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Retrieval System ◽  
Information Overload ◽  
Service Providers ◽  
State Of The Art ◽  
Information Retrieval System ◽  
Movie Recommendation ◽  
Streaming Service

Recommender System or Recommendation Engine gaining popularity as it can tackle information overload problem. Initially it was considered as a domain of Information Retrieval system and was limited to few applications. With the advancement of different state-of-the-art modeling approaches recommender system can be applicable to many application domains. Movie Recommender System (MRS) is widely explored domain and used by many streaming service providers like Netflix, Amazon Prime, YouTube and many more. This system makes use of users’ data to explore and recommends personally as per their taste. In this paper a detailed study on recently published article related to movie recommendation is carried out. Popular techniques for MRS are commonlycategorized into collaborative filtering, content-based and hybridmethod. Neighborhood-based, latent factor based, neural network based and deep learning based techniques have been continuously evolved with application to MRS. Recently proposed models have been reviewed and it is found that hybrid method performs better as compared to individual model.

scholarly journals A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5248
Author(s):  
Aleksandra Pawlicka ◽  
Marek Pawlicki ◽  
Rafał Kozik ◽  
Ryszard S. Choraś
Keyword(s):  
Systematic Review ◽  
Recommender Systems ◽  
Recommender System ◽  
State Of The Art ◽  
The State ◽  
Advantages And Disadvantages ◽  
Comprehensive Survey ◽  
Security Concerns ◽  
Valuable Role

This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of the art concerning the use of recommender systems in cybersecurity; both the existing solutions and future ideas are presented. The contribution of this paper is two-fold: to date, to the best of our knowledge, there has been no work collecting the applications of recommenders for cybersecurity. Moreover, this paper attempts to complete a comprehensive survey of recommender types, after noticing that other works usually mention two–three types at once and neglect the others.

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