scholarly journals Evaluating recommender systems from the user’s perspective: survey of the state of the art

2012 ◽  
Vol 22 (4-5) ◽  
pp. 317-355 ◽  
Author(s):  
Pearl Pu ◽  
Li Chen ◽  
Rong Hu
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
The State

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.

scholarly journals User-Involved Preference Elicitation for Product Search and Recommender Systems

AI Magazine ◽  
2008 ◽  
Vol 29 (4) ◽  
pp. 93 ◽  
Author(s):  
Pearl Pu ◽  
Li Chen
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
Preference Elicitation ◽  
Design Guidelines ◽  
User Preferences ◽  
The State ◽  
User Preference ◽  
Large Collection ◽  
Complete Model ◽  
Product Search

We address user system interaction issues in product search and recommender systems: how to help users select the most preferential item from a large collection of alternatives. As such systems must crucially rely on an accurate and complete model of user preferences, the acquisition of this model becomes the central subject of our paper. Many tools used today do not satisfactorily assist users to establish this model because they do not adequately focus on fundamental decision objectives, help them reveal hidden preferences, revise conflicting preferences, or explicitly reason about tradeoffs. As a result, users fail to find the outcomes that best satisfy their needs and preferences. In this article, we provide some analyses of common areas of design pitfalls and derive a set of design guidelines that assist the user in avoiding these problems in three important areas: user preference elicitation, preference revision, and explanation interfaces. For each area, we describe the state-of-the-art of the developed techniques and discuss concrete scenarios where they have been applied and tested.

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 The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

Data ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 18
Author(s):  
Deepani B. Guruge ◽  
Rajan Kadel ◽  
Sharly J. Halder
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
Current Knowledge ◽  
Research Literature ◽  
The State ◽  
Knowledge Based ◽  
Emerging Trends ◽  
Wide Range ◽  
Research Findings ◽  
Research Outcome

In recent years, education institutions have offered a wide range of course selections with overlaps. This presents significant challenges to students in selecting successful courses that match their current knowledge and personal goals. Although many studies have been conducted on Recommender Systems (RS), a review of methodologies used in course RS is still insufficiently explored. To fill this literature gap, this paper presents the state of the art of methodologies used in course RS along with the summary of the types of data sources used to evaluate these techniques. This review aims to recognize emerging trends in course RS techniques in recent research literature to deliver insights for researchers for further investigation. We provide a systematic review process followed by research findings on the current methodologies implemented in different course RS in selected research journals such as: collaborative, content-based, knowledge-based, Data Mining (DM), hybrid, statistical and Conversational RS (CRS). This study analyzed publications between 2016 and June 2020, in three repositories; IEEE Xplore, ACM, and Google Scholar. These papers were explored and classified based on the methodology used in recommending courses. This review has revealed that there is a growing popularity in hybrid course RS and followed by DM techniques in recent publications. However, few CRS-based course RS were present in the selected publications. Finally, we discussed future avenues based on the research outcome, which might lead to next-generation course RS.

Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions

2010 ◽  
Vol 33 (3) ◽  
pp. 187-209 ◽  
Author(s):  
Aleksandra Klasnja Milicevic ◽  
Alexandros Nanopoulos ◽  
Mirjana Ivanovic
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
The State ◽  
Social Tagging

scholarly journals The state-of-the-art in personalized recommender systems for social networking

2011 ◽  
Vol 37 (2) ◽  
pp. 119-132 ◽  
Author(s):  
Xujuan Zhou ◽  
Yue Xu ◽  
Yuefeng Li ◽  
Audun Josang ◽  
Clive Cox
Keyword(s):  
Social Networking ◽  
Recommender Systems ◽  
State Of The Art ◽  
The State

Recommender systems based on user reviews: the state of the art

2015 ◽  
Vol 25 (2) ◽  
pp. 99-154 ◽  
Author(s):  
Li Chen ◽  
Guanliang Chen ◽  
Feng Wang
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
The State ◽  
User Reviews

A Hybrid Recommender System Using KNN and Clustering

2021 ◽  
Vol 20 (02) ◽  
pp. 553-596
Author(s):  
Hao Fan ◽  
Kaijun Wu ◽  
Hamid Parvin ◽  
Akram Beigi ◽  
Kim-Hung Pho
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
Cold Start ◽  
The State ◽  
Text Segment ◽  
Art Methods ◽  
Additional Process ◽  
High Scalability ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Recommender Systems ([Formula: see text]) are known in the E-Commerce ([Formula: see text]) field. They are expected to suggest the accurate goods/musics/films/items to the consumers/clients/people/users. Recent Hybrid [Formula: see text]s ([Formula: see text] have made us able to deal with the most important shortages of traditional Content-based F iltering ([Formula: see text]) and Collaborative Filtering ([Formula: see text]). Cold start, scalability and sparsity are the most important challenges to [Formula: see text] recommender systems ([Formula: see text]). [Formula: see text]s combine [Formula: see text] and [Formula: see text]. While the [Formula: see text]s that are based on memory have high accuracy, they are not scalable. Contrarily, the RSs on the basis of models have low accuracy but high scalability. Thus, aiming at dealing with cold start, scalability and sparsity challenges, [Formula: see text] is proposed to use both methods and also it has been evaluated on a real benchmark. An ontology, which is automatically created by an intelligently collected wordnet, has been employed in [Formula: see text] segment of the proposed [Formula: see text]. It has been automatically created and enhanced by an additional process. The functionality of the recommended framework has been superior to the performance of the state-of-the-art methods and the traditional [Formula: see text] and [Formula: see text] embedded in our method. Using a real dataset as a benchmark, the experimentations indicate that the proposed method not only has better performance but also has more efficacy rather than the state-of-the-art methods.

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