scholarly journals Community Detection-Based Recommendation Framework

2018 ◽  
pp. 61-66
Author(s):  
Karen Mkhitaryan
Keyword(s):  
Computational Complexity ◽  
Recommender Systems ◽  
Community Detection ◽  
Recommender System ◽  
Information Filtering ◽  
Detection Methods

Recommender system is a type of information filtering system predicting users preferences about items, aiming to generate personalized recommendations. Various recommendation approaches exist in the literature that differ in terms of methodology and types of systems they can be utilized on. In recent years some attempts have been made to incorporate community detection methods into recommender systems to make the process of recommendation generation more accurate in terms of rating or preference prediction and efficient in terms of computational complexity. In this paper we propose a community detection-based approach for recommender system, which is more reasonable in certain applications.

Recommending Academic Papers for Learning Based on Information Filtering Applied to Mobile Environments

2015 ◽  
pp. 2159-2178
Author(s):  
Sílvio César Cazella ◽  
Jorge Luiz Victória Barbosa ◽  
Eliseo Berni Reategui ◽  
Patricia Alejandra Behar ◽  
Otavio Costa Acosta
Keyword(s):  
Learning Environment ◽  
Recommender Systems ◽  
Mobile Devices ◽  
Mobile Learning ◽  
Recommender System ◽  
Information Filtering ◽  
Likert Scale ◽  
Learning Objects ◽  
Mobile Environments ◽  
Proposed Model

Mobile learning is about increasing learners' capability to carry their own learning environment along with them. Recommender Systems are widely used nowadays, especially in e-commerce sites and mobile devices, for example, Amazon.com and Submarino.com. In this chapter, the authors propose the use of such systems in the area of education, specifically for the recommendation of learning objects in mobile devices. The advantage of using Recommender Systems in mobile devices is that it is an easy way to deliver recommendations to students. Based on this scenario, this chapter presents a model of a recommender system based on information filtering for mobile environments. The proposed model was implemented in a prototype aimed to recommend learning objects in mobile devices. The evaluation of the received recommendations was conducted using a Likert scale of 5 points. At the end of this chapter, some future works are described.

scholarly journals A Modularity Degree Based Heuristic Community Detection Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Dongming Chen ◽  
Dongqi Wang ◽  
Fangzhao Xia
Keyword(s):  
Computational Complexity ◽  
Community Detection ◽  
Basic Problem ◽  
Optimization Methods ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Resolution Limit ◽  
Measure Function ◽  
Optimization Function ◽  
Community Detection Algorithm

A community in a complex network can be seen as a subgroup of nodes that are densely connected. Discovery of community structures is a basic problem of research and can be used in various areas, such as biology, computer science, and sociology. Existing community detection methods usually try to expand or collapse the nodes partitions in order to optimize a given quality function. These optimization function based methods share the same drawback of inefficiency. Here we propose a heuristic algorithm (MDBH algorithm) based on network structure which employs modularity degree as a measure function. Experiments on both synthetic benchmarks and real-world networks show that our algorithm gives competitive accuracy with previous modularity optimization methods, even though it has less computational complexity. Furthermore, due to the use of modularity degree, our algorithm naturally improves the resolution limit in community detection.

scholarly journals Realization of Recommender Framework Based on Community Detection

2019 ◽  
pp. 57-65
Author(s):  
Karen Mkhitaryan
Keyword(s):  
Customer Satisfaction ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Community Detection ◽  
Recommender System ◽  
Relevant Information

Recommender systems play an important role in suggesting relevant information to users based on their available preferences about items. Utilizing a recommender system allows companies to increase revenues, customer satisfaction and enable personalization and discovery. Content-based and collaborative filtering approaches are the most popular techniques in recommender systems predicting users preferences based on “collaborative” data about users and items in the system. However, their use is not justified in certain applications, particularly when user-item collaboration data is very sparse or missing. In this paper, a recommender framework based on community detection is developed outperforming other popular recommendation methods in some applications.

On the integration of Machine Learning algorithms and operations research techniques in the development of a hybrid Recommender System

2021 ◽  
pp. 1-14
Author(s):  
Panagiotis Giannopoulos ◽  
Georgios Kournetas ◽  
Nikos Karacapilidis
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Learning Algorithms ◽  
Information Filtering ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbors ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Recommender Systems is a highly applicable subclass of information filtering systems, aiming to provide users with personalized item suggestions. These systems build on collaborative filtering and content-based methods to overcome the information overload issue. Hybrid recommender systems combine the abovementioned methods and are generally proved to be more efficient than the classical approaches. In this paper, we propose a novel approach for the development of a hybrid recommender system that is able to make recommendations under the limitation of processing small amounts of data with strong intercorrelation. The proposed hybrid solution integrates Machine Learning and Multi-Criteria Decision Analysis algorithms. The experimental evaluation of the proposed solution indicates that it performs better than widely used Machine Learning algorithms such as the k-Nearest Neighbors and Decision Trees.

Recommending Academic Papers for Learning Based on Information Filtering Applied to Mobile Environments

2014 ◽  
pp. 120-140 ◽  
Author(s):  
Sílvio César Cazella ◽  
Jorge Luiz Victória Barbosa ◽  
Eliseo Berni Reategui ◽  
Patricia Alejandra Behar ◽  
Otavio Costa Acosta
Keyword(s):  
Learning Environment ◽  
Recommender Systems ◽  
Mobile Devices ◽  
Mobile Learning ◽  
Recommender System ◽  
Information Filtering ◽  
Likert Scale ◽  
Learning Objects ◽  
Mobile Environments ◽  
Proposed Model

Mobile learning is about increasing learners' capability to carry their own learning environment along with them. Recommender Systems are widely used nowadays, especially in e-commerce sites and mobile devices, for example, Amazon.com and Submarino.com. In this chapter, the authors propose the use of such systems in the area of education, specifically for the recommendation of learning objects in mobile devices. The advantage of using Recommender Systems in mobile devices is that it is an easy way to deliver recommendations to students. Based on this scenario, this chapter presents a model of a recommender system based on information filtering for mobile environments. The proposed model was implemented in a prototype aimed to recommend learning objects in mobile devices. The evaluation of the received recommendations was conducted using a Likert scale of 5 points. At the end of this chapter, some future works are described.

scholarly journals Assessment Methods for Evaluation of Recommender Systems: A Survey

2021 ◽  
Vol 46 (4) ◽  
pp. 393-421
Author(s):  
Madhusree Kuanr ◽  
Puspanjali Mohapatra
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Information Filtering ◽  
Assessment Methods ◽  
Past Behavior ◽  
Large Pool ◽  
Near Future ◽  
The Relationship

Abstract The recommender system (RS) filters out important information from a large pool of dynamically generated information to set some important decisions in terms of some recommendations according to the user’s past behavior, preferences, and interests. A recommender system is the subclass of information filtering systems that can anticipate the needs of the user before the needs are recognized by the user in the near future. But an evaluation of the recommender system is an important factor as it involves the trust of the user in the system. Various incompatible assessment methods are used for the evaluation of recommender systems, but the proper evaluation of a recommender system needs a particular objective set by the recommender system. This paper surveys and organizes the concepts and definitions of various metrics to assess recommender systems. Also, this survey tries to find out the relationship between the assessment methods and their categorization by type.

scholarly journals Modelling community structure and temporal spreading on complex networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Structure ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

scholarly journals Recommender systems in model-driven engineering

2021 ◽  
Author(s):  
Lissette Almonte ◽  
Esther Guerra ◽  
Iván Cantador ◽  
Juan de Lara
Keyword(s):  
Recommender Systems ◽  
Information Filtering ◽  
Research Work ◽  
Model Driven Engineering ◽  
Video Broadcasting ◽  
Model Driven ◽  
Challenges And Opportunities ◽  
Mapping Review ◽  
Development Processes ◽  
Systematic Mapping Review

AbstractRecommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research.

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 Criminal Community Detection Based on Isomorphic Subgraph Analytics

2020 ◽  
Vol 10 (1) ◽  
pp. 164-174
Author(s):  
Theyvaa Sangkaran ◽  
Azween Abdullah ◽  
NZ Jhanjhi
Keyword(s):  
Law Enforcement ◽  
Community Detection ◽  
Traditional Method ◽  
Detection Methods ◽  
Law Enforcement Agencies ◽  
Modus Operandi ◽  
Research Gap ◽  
New Perspective ◽  
Key Participants ◽  
Investigative Process

AbstractAll highly centralised enterprises run by criminals do share similar traits, which, if recognised, can help in the criminal investigative process. While conducting a complex confederacy investigation, law enforcement agents should not only identify the key participants but also be able to grasp the nature of the inter-connections between the criminals to understand and determine the modus operandi of an illicit operation. We studied community detection in criminal networks using the graph theory and formally introduced an algorithm that opens a new perspective of community detection compared to the traditional methods used to model the relations between objects. Community structure, generally described as densely connected nodes and similar patterns of links is an important property of complex networks. Our method differs from the traditional method by allowing law enforcement agencies to be able to compare the detected communities and thereby be able to assume a different viewpoint of the criminal network, as presented in the paper we have compared our algorithm to the well-known Girvan-Newman. We consider this method as an alternative or an addition to the traditional community detection methods mentioned earlier, as the proposed algorithm allows, and will assists in, the detection of different patterns and structures of the same community for enforcement agencies and researches. This methodology on community detection has not been extensively researched. Hence, we have identified it as a research gap in this domain and decided to develop a new method of criminal community detection.

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