PageRank Algorithm-Based Recommender System Using Uniformly Average Rating Matrix

2019 ◽  
pp. 99-112
Author(s):  
Bathrinath S. ◽  
Saranyadevi S. ◽  
Thirumalai Kumaran S. ◽  
Saravanasankar S.
Keyword(s):  
Recommender Systems ◽  
Large Scale ◽  
User Behavior ◽  
Personalized Recommendation ◽  
Pagerank Algorithm ◽  
Large Scale Networks ◽  
Matrix Construction ◽  
Item Item ◽  
New Item

Applications of web data mining is the prediction of user behavior with respect to items. Recommender systems are being applied in knowledge discovery techniques to the problem of making decisions on personalized recommendation of information. Traditional CF approaches involve the amount of effort increases with number of users. Hence, new recommender systems need to be developed to process high quality recommendations for large-scale networks. In this chapter, a model for UAR matrix construction method for item rank calculations, a Page Rank-based item ranking approach are proposed. The analysis of various techniques for computing item-item similarities to identify relationship between the selected items and to produce a qualified recommendation for users to acquire the items as their wish. As a result, the new item rank-based approaches improve the quality of recommendation outcome. Results show that the proposed UAR method outperforms than the existing method. The same method is applied for the large real-time rating dataset like Movie Lens.

scholarly journals A Survey of Methods for Gas-Lift Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Kashif Rashid ◽  
William Bailey ◽  
Benoît Couët
Keyword(s):  
Large Scale ◽  
Optimization Problem ◽  
Multivariate Optimization ◽  
Flow Lines ◽  
Single Well ◽  
Large Scale Networks ◽  
Optimization Schemes ◽  
Gas Lift ◽  
Insight Into

This paper presents a survey of methods and techniques developed for the solution of the continuous gas-lift optimization problem over the last two decades. These range from isolated single-well analysis all the way to real-time multivariate optimization schemes encompassing all wells in a field. While some methods are clearly limited due to their neglect of treating the effects of inter-dependent wells with common flow lines, other methods are limited due to the efficacy and quality of the solution obtained when dealing with large-scale networks comprising hundreds of difficult to produce wells. The aim of this paper is to provide an insight into the approaches developed and to highlight the challenges that remain.

scholarly journals Analisis Perbandingan Quality Of Service (Qos) Pada Performa Bandwidth Jaringan Dengan Metode Hierarchical Token Bucket (Htb) Dan Per Connection Queque (Pcq).

Respati ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. 24
Author(s):  
Adytia A. Tambunan ◽  
Lukman Lukman
Keyword(s):  
Quality Of Service ◽  
Large Scale ◽  
Improve Quality ◽  
Bandwidth Management ◽  
Available Bandwidth ◽  
Service Parameter ◽  
Token Bucket ◽  
Large Scale Networks ◽  
Network Administrators

INTISARIQuality of Service (QoS) adalah cara cerdas untuk mengalokasikan bandwidth yang tersedia. Penggunaan manajemen bandwidth sebagai parameter Quality of Service tidak hanya membatasi tetapi menjaga kualitas bandwidth, sehingga semua pengguna yang terhubung dalam satu jaringan mendapatkan kualitas internet yang merata dan stabil. Ada beberapa cara untuk mengaplikasikan bandwidth managemen untuk meningkatkan Quality of Service, salah satunya yakni menggunakan mikrotik.Ada banyak metode manajemen   bandwidth yang dapat digunakan atau diterapkan pada jaringan yang menggunakan router mikrotik. Adapun yang paling sering digunakan dalam jaringan berskala menengah atau pun besar seperti metode Hierarchical Token Bucket (HTB) dan Per Connection Queue (PCQ).Penelitian ini akan melakukan analisis variabel Quality of Service (QoS) terhadap performa bandwidth dengan membandingkan dua metode antrian yaitu metode Hierarchical Token Bucket (HTB) dan metode Per Connection Queue (PCQ).  Penelitian dilakukan untuk mengetahui metode manakah lebih baik untuk di implementasikan oleh administrator jaringan agar efesien dan tepat untuk digunakan.Kata kunci: Manajemen Bandwidth, QOS, PCQ, HTB, Mikrotik. ABSTRACTQuality of Service (QoS) is a smart way to allocate available bandwidth. The use of bandwidth management as a Quality of Service parameter not only limits but maintains bandwidth quality, so that all users connected in one network get an even and stable internet quality. There are several ways to apply bandwidth management to improve Quality of Service, one of which is using a proxy.There are many bandwidth management methods that can be used or applied to networks that use a proxy router. As for what is most often used in medium or large scale networks such as the Hierarchical Token Bucket (HTB) and Per Connection Queue (PCQ) methods.This study will analyze the Quality of Service (QoS) variable on bandwidth performance by comparing two queuing methods, namely the Hierarchical Token Bucket (HTB) method and the Per Connection Queue (PCQ) method. The study was conducted to determine which method is better for network administrators to implement in order to be efficient and appropriate to use.Keywords: Bandwidth Management, QOS, PCQ, HTB, Mikrotik.

scholarly journals Examining the Usefulness of Quality Scores for Generating Learning Object Recommendations in Repositories of Open Educational Resources

2020 ◽  
Vol 10 (13) ◽  
pp. 4638 ◽  
Author(s):  
Aldo Gordillo ◽  
Daniel López-Fernández ◽  
Katrien Verbert
Keyword(s):  
Recommender Systems ◽  
User Study ◽  
Hybrid Approach ◽  
Open Educational Resources ◽  
Educational Resources ◽  
Learning Object ◽  
Personalized Recommendation ◽  
Effective Learning ◽  
Pedagogical Quality

Open educational resources (OER) can contribute to democratize education by providing effective learning experiences with lower costs. Nevertheless, the massive amount of resources currently available in OER repositories makes it difficult for teachers and learners to find relevant and high-quality content, which is hindering OER use and adoption. Recommender systems that use data related to the pedagogical quality of the OER can help to overcome this problem. However, studies analyzing the usefulness of these data for generating OER recommendations are very limited and inconclusive. This article examines the usefulness of using pedagogical quality scores for generating OER recommendations in OER repositories by means of a user study that compares the following four different recommendation approaches: a traditional content-based recommendation technique, a quality-based non-personalized recommendation technique, a hybrid approach that combines the two previous techniques, and random recommendations. This user study involved 53 participants and 400 OER whose quality was evaluated by reviewers using the Learning Object Review Instrument (LORI). The main finding of this study is that pedagogical quality scores can enhance traditional content-based OER recommender systems by allowing them to recommend OER with more quality without detriment to relevance.

scholarly journals A Survey of Outlier Detection Techniques in IoT: Review and Classification

2022 ◽  
Vol 11 (1) ◽  
pp. 4
Author(s):  
Mustafa Al Samara ◽  
Ismail Bennis ◽  
Abdelhafid Abouaissa ◽  
Pascal Lorenz
Keyword(s):  
Outlier Detection ◽  
Large Scale ◽  
Base Station ◽  
Home Networks ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Large Scale Networks ◽  
Data Efficiency ◽  
Different Sources

The Internet of Things (IoT) is a fact today where a high number of nodes are used for various applications. From small home networks to large-scale networks, the aim is the same: transmitting data from the sensors to the base station. However, these data are susceptible to different factors that may affect the collected data efficiency or the network functioning, and therefore the desired quality of service (QoS). In this context, one of the main issues requiring more research and adapted solutions is the outlier detection problem. The challenge is to detect outliers and classify them as either errors to be ignored, or important events requiring actions to prevent further service degradation. In this paper, we propose a comprehensive literature review of recent outlier detection techniques used in the IoTs context. First, we provide the fundamentals of outlier detection while discussing the different sources of an outlier, the existing approaches, how we can evaluate an outlier detection technique, and the challenges facing designing such techniques. Second, comparison and discussion of the most recent outlier detection techniques are presented and classified into seven main categories, which are: statistical-based, clustering-based, nearest neighbour-based, classification-based, artificial intelligent-based, spectral decomposition-based, and hybrid-based. For each category, available techniques are discussed, while highlighting the advantages and disadvantages of each of them. The related works for each of them are presented. Finally, a comparative study for these techniques is provided.

Web Content Recommendation Methods Based on Reinforcement Learning

2011 ◽  
pp. 2353-2380
Author(s):  
Nima Taghipour ◽  
Ahmad Kardan
Keyword(s):  
Reinforcement Learning ◽  
Recommender Systems ◽  
Information Overload ◽  
User Behavior ◽  
Web Content ◽  
Web Usage ◽  
Content Recommendation ◽  
Conceptual Relationships ◽  
The Web

Information overload is no longer news; the explosive growth of the Internet has made this issue increasingly serious for Web users. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests. In this chapter we introduce our novel machine learning perspective toward the web recommendation problem, based on reinforcement learning. Our recommendation method makes use of the web usage and content data to learn a predictive model of users’ behavior on the web and exploits the learned model to make web page recommendations. Unlike other recommender systems, our system does not use the static patterns discovered from web usage data, instead it learns to make recommendations as the actions it performs in each situation. In the proposed method we combined the conceptual and usage information in order to gain a more general model of user behavior and improve the quality of web recommendations. A hybrid web recommendation method is proposed by making use of the conceptual relationships among web resources to derive a novel model of the problem, enriched with semantic knowledge about the usage behavior. The method is evaluated under different settings and it is shown how this method can improve the overall quality of recommendations.

Web Content Recommendation Methods Based on Reinforcement Learning

2010 ◽  
pp. 222-249
Author(s):  
Nima Taghipour ◽  
Ahmad Kardan
Keyword(s):  
Reinforcement Learning ◽  
Recommender Systems ◽  
Information Overload ◽  
User Behavior ◽  
Web Content ◽  
Web Usage ◽  
Content Recommendation ◽  
Conceptual Relationships ◽  
The Web

Information overload is no longer news; the explosive growth of the Internet has made this issue increasingly serious for Web users. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests. In this chapter the authors introduce their novel machine learning perspective toward the Web recommendation problem, based on reinforcement learning. Our recommendation method makes use of the Web usage and content data to learn a predictive model of users’ behavior on the Web and exploits the learned model to make Web page recommendations. Unlike other recommender systems, this system does not use the static patterns discovered from Web usage data, instead it learns to make recommendations as the actions it performs in each situation. In the proposed method the authors combined the conceptual and usage information in order to gain a more general model of user behavior and improve the quality of web recommendations. A hybrid Web recommendation method is proposed by making use of the conceptual relationships among Web resources to derive a novel model of the problem, enriched with semantic knowledge about the usage behavior. The method is evaluated under different settings and it is shown how this method can improve the overall quality of recommendations.

Personalized Recommendation Based on Contextual Awareness and Tensor Decomposition

2018 ◽  
Vol 16 (3) ◽  
pp. 39-51
Author(s):  
Zhenjiao Liu ◽  
Xinhua Wang ◽  
Tianlai Li ◽  
Lei Guo
Keyword(s):  
Recommendation System ◽  
Tensor Decomposition ◽  
Personalized Recommendation ◽  
Third Order ◽  
Recommendation Algorithm ◽  
Singular Value Decomposition Method ◽  
Contextual Awareness ◽  
Value Decomposition ◽  
Item Item

In order to solve users' rating sparsely problem existing in present recommender systems, this article proposes a personalized recommendation algorithm based on contextual awareness and tensor decomposition. Via this algorithm, it was first constructed two third-order tensors to represent six types of entities, including the user-user-item contexts and the item-item-user contexts. And then, this article uses a high order singular value decomposition method to mine the potential semantic association of the two third-order tensors above. Finally, the resulting tensors were combined to reach the recommendation list to respond the users' personalized query requests. Experimental results show that the proposed algorithm can effectively improve the effectiveness of the recommendation system. Especially in the case of sparse data, it can significantly improve the quality of the recommendation.

scholarly journals Critique on Natural Noise in Recommender Systems

2021 ◽  
Vol 15 (5) ◽  
pp. 1-30
Author(s):  
Wissam Al Jurdi ◽  
Jacques Bou Abdo ◽  
Jacques Demerjian ◽  
Abdallah Makhoul
Keyword(s):  
Recommender Systems ◽  
User Behavior ◽  
Cold Start ◽  
User Experiences ◽  
Data Sparsity ◽  
Natural Noise ◽  
E Learning ◽  
Management Techniques ◽  
Noise Management

Recommender systems have been upgraded, tested, and applied in many, often incomparable ways. In attempts to diligently understand user behavior in certain environments, those systems have been frequently utilized in domains like e-commerce, e-learning, and tourism. Their increasing need and popularity have allowed the existence of numerous research paths on major issues like data sparsity, cold start, malicious noise, and natural noise, which immensely limit their performance. It is typical that the quality of the data that fuel those systems should be extremely reliable. Inconsistent user information in datasets can alter the performance of recommenders, albeit running advanced personalizing algorithms. The consequences of this can be costly as such systems are employed in abundant online businesses. Successfully managing these inconsistencies results in more personalized user experiences. In this article, the previous works conducted on natural noise management in recommender datasets are thoroughly analyzed. We adequately explore the ways in which the proposed methods measure improved performances and touch on the different natural noise management techniques and the attributes of the solutions. Additionally, we test the evaluation methods employed to assess the approaches and discuss several key gaps and other improvements the field should realize in the future. Our work considers the likelihood of a modern research branch on natural noise management and recommender assessment.

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