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.

Web Usage Mining Issues in Big Data

2021 ◽  
pp. 102-112
Author(s):  
Sunny Sharma ◽  
Manisha Malhotra
Keyword(s):  
Data Mining ◽  
Big Data ◽  
User Behavior ◽  
Web Usage Mining ◽  
Web Personalization ◽  
Data Mining Techniques ◽  
Meaningful Information ◽  
Web Usage ◽  
Use Of Data ◽  
The Web

Web usage mining is the use of data mining techniques to analyze user behavior in order to better serve the needs of the user. This process of personalization uses a set of techniques and methods for discovering the linking structure of information on the web. The goal of web personalization is to improve the user experience by mining the meaningful information and presented the retrieved information in a way the user intends. The arrival of big data instigated novel issues to the personalization community. This chapter provides an overview of personalization, big data, and identifies challenges related to web personalization with respect to big data. It also presents some approaches and models to fill the gap between big data and web personalization. Further, this research brings additional opportunities to web personalization from the perspective of big data.

DSS for Web Mining Using Recommendation System

2017 ◽  
pp. 22-34 ◽  
Author(s):  
Varaprasad Rao M ◽  
Vishnu Murthy G
Keyword(s):  
Recommender Systems ◽  
Web Mining ◽  
Recommendation System ◽  
User Behavior ◽  
Web Content ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
Computer Based ◽  
The Many

Decision Supports Systems (DSS) are computer-based information systems designed to help managers to select one of the many alternative solutions to a problem. A DSS is an interactive computer based information system with an organized collection of models, people, procedures, software, databases, telecommunication, and devices, which helps decision makers to solve unstructured or semi-structured business problems. Web mining is the application of data mining techniques to discover patterns from the World Wide Web. Web mining can be divided into three different types – Web usage mining, Web content mining and Web structure mining. Recommender systems (RS) aim to capture the user behavior by suggesting/recommending users with relevant items or services that they find interesting in. Recommender systems have gained prominence in the field of information technology, e-commerce, etc., by inferring personalized recommendations by effectively pruning from a universal set of choices that directed users to identify content of interest.

Specification of the Context of Use for the Development of Web-Based Applications

2011 ◽  
pp. 146-162
Author(s):  
Marta Fernández de Arriba ◽  
Eugenia Díaz ◽  
Jesús Rodríguez Pérez
Keyword(s):  
Web Applications ◽  
Assistive Technologies ◽  
Web Content ◽  
Disabled People ◽  
Technological Environment ◽  
Web Based ◽  
Development Team ◽  
Context Of Use ◽  
The Web

This chapter presents the structure of an index which serves as support so allowing the development team to create the specification of the context of use document for the development of Web applications, bearing in mind characteristics of usability and accessibility, each point of the index being explained in detail. A correct preparation of this document ensures the quality of the developed Web applications. The international rules and standards related to the identification of the context of use have been taken into account. Also, the functionality limitations (sensorial, physical, or cognitive) which affect access to the Web are described, as well as the technological environment used by disabled people (assistive technologies or alternative browsers) to facilitate their access to the Web content. Therefore, following the developed specification of the context of use, usable and accessible Web applications with their corresponding benefits can be created.

They Know What You Will Do Next Click

2020 ◽  
pp. 100-122
Author(s):  
Serra Çelik
Keyword(s):  
Focus Group ◽  
User Behavior ◽  
Web Usage Mining ◽  
Web Log ◽  
Web Usage ◽  
User Behaviors ◽  
Log Files ◽  
The Web

This chapter focuses on predicting web user behaviors. When web users enter a website, every move they make on that website is stored as web log files. Unlike the focus group or questionnaire, the log files reflect real user behavior. It can easily be said that having actual user behavior is a gold value for the organizations. In this chapter, the ways of extracting user patterns (user behavior) from the log files are sought. In this context, the web usage mining process is explained. Some web usage mining techniques are mentioned.

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.

Webometric Analysis and Link Relationship of Computer Science and Information Technology Websites of Malaysia Universities

2020 ◽  
Vol 17 (2) ◽  
pp. 1260-1265
Author(s):  
Mohd Sharul Hafiz Razak ◽  
Nor Azman Ismail ◽  
Alif Fikri Mohktar ◽  
Su Elya Namira ◽  
Nurina Izzati Ramzi
Keyword(s):  
Information Technology ◽  
Computer Science ◽  
Rank Correlation ◽  
Web Pages ◽  
Web Content ◽  
Content Strategy ◽  
Relationship Of ◽  
The Relationship ◽  
The Web

This paper aims to investigate 18 web domains of computer science and information technology academic websites of Malaysia universities.We collected more than two million web pages. A webometric analysis was used to explore the number of web pages, inbound links, the web impact factor (WIF) and link relationships. The results show Fakulti Teknologi dan Sains Maklumat (FTSM), Universiti Kebangsaan Malaysia (UKM) has the highest number of webpages while Fakulti Teknologi Kreatif dan Warisan (FTKW), Universiti Malaysia Kelantan (UMK) has the largest WIF score. Pearson’s rank correlation coefficient was used to detect the relationship between institutions subdomain age and WIF. Correlations point out that there is scant relationship between subdomain age and WIF score across all 18 Malaysia selected schools [r =−.076, n = 18, p < .0005]. This is due to WIF are highly dependent on the quality of the content to attract backlinks and Google crawler algorithm that changes from time to time for the number of web pages. Subdomain age is independent to the year of establishment of the schools. These findings can be used as a guide to the implementation of university web content strategy.

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.

Performance Comparison of Data Mining Classifiers on Web Log Data

2020 ◽  
Vol 17 (11) ◽  
pp. 5113-5116
Author(s):  
Varun Malik ◽  
Vikas Rattan ◽  
Jaiteg Singh ◽  
Ruchi Mittal ◽  
Urvashi Tandon
Keyword(s):  
Web Mining ◽  
Performance Comparison ◽  
Web Usage Mining ◽  
Classification Algorithms ◽  
Web Content ◽  
Web Usage ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
The Web

Web usage mining is the branch of web mining that deals with mining of data over the web. Web mining can be categorized as web content mining, web structure mining, web usage mining. In this paper, we have summarized the web usage mining results executed over the user tool WMOT (web mining optimized tool) based on the WEKA tool that has been used to apply various classification algorithms such as Naïve Bayes, KNN, SVM and tree based algorithms. Authors summarized the results of classification algorithms on WMOT tool and compared the results on the basis of classified instances and identify the algorithms that gives better instances accuracy.

WEB FARMING WITH CLICKSTREAM

2008 ◽  
Vol 07 (02) ◽  
pp. 291-308 ◽  
Author(s):  
JIA HU ◽  
NING ZHONG
Keyword(s):  
Behavior Analysis ◽  
Web Mining ◽  
User Interaction ◽  
Purchasing Behavior ◽  
Web Content ◽  
Related Data ◽  
Web Usage ◽  
Proposed Model ◽  
The Web ◽  
Usage Data

In a commercial website or portal, Web information fusion is usually from the following two approaches, one is to integrate the Web content, structure, and usage data for surfing behavior analysis; the other is to integrate Web usage data with traditional customer, product, and transaction data for purchasing behavior analysis. In this paper, we propose a unified model based on Web farming technology for collecting clickstream logs in the whole user interaction process. We emphasize that collecting clickstream logs at the application layer will help to seamlessly integrate Web usage data with other customer-related data sources. In this paper, we extend the Web log standard to modeling clickstream format and Web mining to Web farming from passively collecting data and analyzing the customer behavior to actively influence the customer's decision making. The proposed model can be developed as a common plugin for most existing commercial websites and portals.

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