scholarly journals A Survey of Collaborative Filtering Techniques

2009 ◽  
Vol 2009 ◽  
pp. 1-19 ◽  
Author(s):  
Xiaoyuan Su ◽  
Taghi M. Khoshgoftaar
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Privacy Protection ◽  
State Of The Art ◽  
Predictive Performance ◽  
The State ◽  
Data Sparsity ◽  
Research And Practice ◽  
Model Based ◽  
Comprehensive Survey

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

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.

State-of-the-Art Recommender Systems

2011 ◽  
pp. 1-22 ◽  
Author(s):  
Laurent Candillier ◽  
Kris Jack ◽  
Françoise Fessant ◽  
Frank Meyer
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Hybrid System ◽  
User Satisfaction ◽  
State Of The Art ◽  
Predictive Performance ◽  
Performance Accuracy ◽  
Item Content ◽  
User Ratings ◽  
Content Based Filtering

The aim of Recommender Systems is to help users to find items that they should appreciate from huge catalogues. In that field, collaborative filtering approaches can be distinguished from content-based ones. The former is based on a set of user ratings on items, while the latter uses item content descriptions and user thematic profiles. While collaborative filtering systems often result in better predictive performance, content-based filtering offers solutions to the limitations of collaborative filtering, as well as a natural way to interact with users. These complementary approaches thus motivate the design of hybrid systems. In this chapter, the main algorithmic methods used for recommender systems are presented in a state of the art. The evaluation of recommender systems is currently an important issue. The authors focus on two kinds of evaluations. The first one concerns the performance accuracy: several approaches are compared through experiments on two real movies rating datasets MovieLens and Netflix. The second concerns user satisfaction and for this a hybrid system is implemented and tested with real users.

scholarly journals Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 41782-41798 ◽  
Author(s):  
Santiago Alonso ◽  
Jesus Bobadilla ◽  
Fernando Ortega ◽  
Ricardo Moya
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Model Based ◽  
Robust Model

The state of the art on buffer allocation problem: a comprehensive survey

2012 ◽  
Vol 25 (3) ◽  
pp. 371-392 ◽  
Author(s):  
Leyla Demir ◽  
Semra Tunali ◽  
Deniz Tursel Eliiyi
Keyword(s):  
State Of The Art ◽  
The State ◽  
Allocation Problem ◽  
Buffer Allocation ◽  
Comprehensive Survey

Technical Details and Educational Applications for Virtual Reality Technologies

2021 ◽  
pp. 74-95
Author(s):  
Yongzhi Wang
Keyword(s):  
Higher Education ◽  
Virtual Reality ◽  
Learning Process ◽  
State Of The Art ◽  
The State ◽  
High Quality ◽  
Comprehensive Survey ◽  
Technical Details ◽  
Educational Applications ◽  
Teaching Learning

The application of virtual reality (VR) in higher education has drawn attention. Understanding the state of the art for VR technologies helps educators identify appropriate applications and develop a high-quality engaging teaching-learning process. This chapter provides a comprehensive survey of current hardware and software supports on VR. Secondly, important technical metrics in VR technology are considered with comparisons of different VR devices using identified metrics. Third, there is a focus on software tools and an explore of various development frameworks, which facilitate the implementation of VR applications. With this information as a foundation, there is a VR use in higher education. Finally, there is a discussion of VR applications that can be potentially used in education.

Visual Data Mining for Collaborative Filtering

2017 ◽  
pp. 217-235 ◽  
Author(s):  
Marenglen Biba ◽  
Narasimha Rao Vajjhala ◽  
Lediona Nishani
Keyword(s):  
Data Mining ◽  
Collaborative Filtering ◽  
State Of The Art ◽  
The State ◽  
Visual Exploration ◽  
Visual Data ◽  
Visual Data Mining ◽  
Data Mining Techniques ◽  
Existing Data

This book chapter provides a state-of-the-art survey of visual data mining techniques used for collaborative filtering. The chapter begins with a discussion on various visual data mining techniques along with an analysis of the state-of-the-art visual data mining techniques used by researchers as well as in the industry. Collaborative filtering approaches are presented along with an analysis of the state-of-the-art collaborative filtering approaches currently in use in the industry. Visual data mining can provide benefit to existing data mining techniques by providing the users with visual exploration and interpretation of data. The users can use these visual interpretations for further data mining. This chapter dealt with state-of-the-art visual data mining technologies that are currently in use apart. The chapter also includes the key section of the discussion on the latest trends in visual data mining for collaborative filtering.

Visual Data Mining for Collaborative Filtering

2017 ◽  
pp. 1274-1292
Author(s):  
Marenglen Biba ◽  
Narasimha Rao Vajjhala ◽  
Lediona Nishani
Keyword(s):  
Data Mining ◽  
Collaborative Filtering ◽  
State Of The Art ◽  
The State ◽  
Visual Exploration ◽  
Visual Data ◽  
Visual Data Mining ◽  
Data Mining Techniques ◽  
Existing Data

This book chapter provides a state-of-the-art survey of visual data mining techniques used for collaborative filtering. The chapter begins with a discussion on various visual data mining techniques along with an analysis of the state-of-the-art visual data mining techniques used by researchers as well as in the industry. Collaborative filtering approaches are presented along with an analysis of the state-of-the-art collaborative filtering approaches currently in use in the industry. Visual data mining can provide benefit to existing data mining techniques by providing the users with visual exploration and interpretation of data. The users can use these visual interpretations for further data mining. This chapter dealt with state-of-the-art visual data mining technologies that are currently in use apart. The chapter also includes the key section of the discussion on the latest trends in visual data mining for collaborative filtering.

Spam 2.0 State of the Art

2012 ◽  
Vol 4 (1) ◽  
pp. 17-36 ◽  
Author(s):  
Pedram Hayati ◽  
Vidyasagar Potdar
Keyword(s):  
Early Detection ◽  
Web 2.0 ◽  
Social Networking ◽  
State Of The Art ◽  
The State ◽  
Online Forums ◽  
Mass Content ◽  
Social Networking Websites ◽  
Comprehensive Survey

Spam 2.0 is defined as the propagation of unsolicited, anonymous, mass content to infiltrate legitimate Web 2.0 applications. A fake eye-catching profile in social networking websites, a promotional review, a response to a thread in online forums with unsolicited content, or a manipulated Wiki page are examples of Spam 2.0. In this paper, the authors provide a comprehensive survey of the state-of-the-art, detection-based, prevention-based and early-detection-based Spam 2.0 filtering methods.

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