Intelligent techniques in recommendation systems: contextual advancements and new methods
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
2013
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | "Premier reference source"--Cover. Includes bibliographical references and index -- "This book is a comprehensive collection of research on the latest advancements of intelligence techniques and their application to recommendation systems and how they could improve this field of study"-- Provided by publisher. |
| Beschreibung: | xvii, 332 pages illustrations 29 cm |
| ISBN: | 9781466625426 |
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Datensatz im Suchindex
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| adam_text | Titel: Intelligent techniques in recommendation systems
Autor: Dehuri, Satchidananda
Jahr: 2013
Detailed Table of Contents
Preface................................................................................................................................................xiii
Chapter 1
A Recommender System Supporting Teachers to Author Learning Sessions in
Decision-Making....................................................................................................................................1
Arnoldo Rodriguez, Universidad de Costa Rica, Costa Rica
This chapter pays attention to the automatic generation and recommendation of teaching materials for
teachers who do not have enough time to learn how to use authoring tools for the creation of materials to
support their courses. To overcome the difficulties, the research is intended to solve the problem of time
needed to create adapted case studies for teaching decision-making in network design. Another goal is
to reduce the time required to learn the use of an authoring tool to create teaching materials. Thus, the
authors present an assistant that provides adapted help for teachers, generates examples automatically,
verifies that any generated example fits in the class of examples used by the teacher, and recommends
personalized examples according to each teacher s preferences. They study the use of data related to
teachers to support the recommendation of teaching materials and the adaptation of Web-based support.
The automatic generation and test of examples of network topologies are based on a probabilistic model,
and the recommendation is based on Bayesian classification. This investigation also looks at problems
related to the application of Artificial Intelligence (Al) to support teachers in authoring learning sessions
for Adaptive Educational Hypermedia (AEH).
Chapter 2
Building Efficient Assessment Applications with Personalized Feedback: A Model for Requirement
Specifications .......................................................................................................................................30
Constanta-Nicoleta Bodea, Academy of Economic Studies, Romania
Maria-Iuliana Dascalu, Academy of Economic Studies, Romania
The aim of this chapter is to provide a model for requirement specification, useful in developing efficient
e-assessment applications with personalized feedback, which is enhanced by calling a recommender
engine. The research was done in the context of using educational technology to facilitate learning
processes. The data used to build the requirement model was collected from a set of interviews with the
users and creators of an e-assessment application in project management. Requirement analysis assumes
human effort and thus introduces uncertainties. To minimize the subjective factor, the data extracted
from interviews with the users and the developers ofthe existing e-assessment application are clustered
using a fuzzy logic solution into classes of requirements. These classes are the units ofthe model. The
connections between classes are also mentioned: relations such as if-then, switch, or contains are
explained. The requirements analysis conducts a smart set of specifications, obtained in a collaborative
manner, useful forthe design of e-assessment applications in project management or othersimilar domains.
Chapter 3
Building Recommender Systems for Network Intrusion Detection Using Intelligent Decision
Technologies ........................................................................................................................................49
Mrutyunjaya Panda, Gandhi Institute for Technological Advancement (GITA), India
Manas Ranjan Patra, Berhampur University, India
Sachidananda Dehuri, Fakir Mohan University, India
This chapter presents an overview ofthe field of recommender systems and describes the current gen-
eration of recommendation methods with their limitations and possible extensions that can improve the
capabilities of the recommendations made suitable for a wide range of applications. In recent years,
machine learning algorithms have been considered to be an important part of the recommendation
process to take intelligent decisions. The chapter will explore the application of such techniques in the
field of network intrusion detection in order to examine the vulnerabilities of different recommendation
techniques. Finally, the authors outline some ofthe major issues in building secure recommendation
systems in identifying possible network intrusions.
Chapter 4
REPERIO: A Flexible Architecture for Recommendation in an Industrial Context............................63
Frank Meyer, Orange Labs Lannion, France
Damien Poirier, LIFO Orleans, France
Isabelle Tellier, LIFO Orleans, France
Francoise Fessant, Orange Labs Lannion, France
In this chapter, the authors describe Reperio, a flexible and generic industrial recommender system able
to deal with several kinds of data sources (content-based, collaborative, social network) in the same
framework and to work on multi-platforms (Web service in a multi-user mode and mobile device in a
mono-user mode). The item-item matrix is the keystone ofthe architecture for its efficiency and flexibility
properties. In the first part, the authors present core functionalities and requirements of recommendation
in an industrial context. In the second part, they present the architecture ofthe system and the main is-
sues involved in its development. In the last part, the authors report experimental results obtained using
Reperio on benchmarks extracted from the Netflix Prize with different filtering strategies. To illustrate
the interest and flexibility ofthe architecture, they also explain how it is possible to take into account,
for recommendations, external sources of information. In particular, the authors show how to exploit
user generated contents posted on social networks to fill the item-item matrix. The process proposed
includes a step of opinion classification.
Chapter 5
Risk Evaluation in the Insurance Company Using REFH Model........................................................84
Goran Klepac, Raiffeisen Bank Austria, Croatia
A business case describes a problem present in all insurance companies: portfolio risk evaluation. Such
analysis deals with determining the risk level as well as main risk factors. In the specific case, an insur-
ance company is faced with market share growth and profit decline. Discovered knowledge about the
level of risk and main risk factors was not used to increase premium for the riskiest portfolio segments
due to a specific market situation, which could lead to loss of clients in the long run. Instead, additional
analysis was conducted using data mining methods resulting in a solution, which stopped further profit
decline and lowered the risk level for the riskiest portfolio segments. The central role for the unexpected
revealed knowledge in the chapter acts as the REFII model. The REFII model is an authorial mathematical
model for time series data mining. The main purpose of that model is to automate time series analysis,
through a unique transformation model of time series.
Chapter 6
Intelligent Techniques in Recommender Systems and Contextual Advertising: Novel Approaches and
Case Studies.......................................................................................................................................105
Giuliano Armano, University ofCagliari, Italy
Alessandro Giuliani, University ofCagliari, Italy
Eloisa Vargiu, University ofCagliari, Italy Barcelona Digital Technology Center, Spain
Information Filtering deals with the problem of selecting relevant information for a given user, accord-
ing to her/his preferences and interests. In this chapter, the authors consider two ways of performing
information filtering: recommendation and contextual advertising. In particular, they study and analyze
them according to a unified view. In fact, the task of suggesting an advertisement to a Web page can
be viewed as the task of recommending an item (the advertisement) to a user (the Web page), and vice
versa. Starting from this insight, the authors propose a content-based recommender system based on a
generic solution for contextual advertising and a hybrid contextual advertising system based on a generic
hybrid recommender system. Relevant case studies have been considered (i.e., a photo recommender
and a Web advertiser) with the goal of highlighting how the proposed approach works in practice. In
both cases, results confirm the effectiveness ofthe proposed solutions.
Chapter 7
Revisiting Recommendation Systems: Development, Lacunae, and Proposal for
Hybridization .....................................................................................................................................129
Sagarika Bakshi, ITER, SOA University, India
Sweta Sarkar, ITER, SOA University, India
Alok Kumar Jagadev, ITER, SOA University, India
Satchidananda Dehuri, Fakir Mohan University, India
Recommender systems are applied in a multitude of spheres and have a significant role in reduction of
information overload on those websites that have the features of voting. Therefore, it is an urgent need
for them to adapt and respond to immediate changes in user preference. To overcome the shortcomings
of each individual approach to design the recommender systems, a myriad of ways to coalesce different
recommender systems are proposed by researchers. In this chapter, the authors have presented an insight
into the design of recommender systems developed, namely content-based and collaborative recommen-
dations, their evaluation, their lacunae, and some hybrid models to enhance the quality of prediction.
Chapter 8
A Fuzzy Clustering Approach for Segmenting Retail Industry .........................................................152
M. Hemalatha, M.A.M. College of Engineering, India
The foremost theme of this chapter is to utilize the subtractive clustering concept for defining the mar-
ket boundaries in the fuzzy-based segmentation. In this sense, the present work starts by analyzing the
importance of segmenting the shoppers on the basis of store image. After reviewing the segmentation
literature, the authors performed a segmentation analysis of retail shoppers in India. Researchers often
use clustering analysis as a tool in market segmentation studies, the results of which often end with a
crisp partitioning form, where one member cannot belong to two or more groups. This indicates that
different segments overlap with one another. This chapter integrates the concept of application of sub-
tractive clustering in fuzzy c means clustering for profiling the customers who perceive the retail store
based on its image. Fuzzy clustering is also compared with hard clustering solutions. Then the authors
predict the model using discriminate analysis. Further, the chapter concentrates on the answer tree
model of segmentation to identify the best predictor. Main conclusions with implications for retailing
management are shown.
Chapter 9
Rough Web Intelligent Techniques for Page Recommendation.........................................................170
H. Hannah lnbarani, Periyar University, India
K Thangavel, Periyar University, India
Recommender systems represent a prominent class of personalized Web applications, which particularly
focus on the user-dependent filtering and selection of relevant information. Recommender Systems
have been a subject of extensive research in Artificial Intelligence over the last decade, but with today s
increasing number of e-commerce environments on the Web, the demand for new approaches to intel-
ligent product recommendation is higher than ever. There are more online users, more online channels,
more vendors, more products, and, most importantly, increasingly complex products and services. These
recent developments in the area of recommender systems generated new demands, in particular with
respect to interactivity, adaptivity, and user preference elicitation. These challenges, however, are also
in the focus of general Web page recommendation research. The goal of this chapter is to develop robust
techniques to model noisy data sets containing an unknown number of overlapping categories and apply
them for Web personalization and mining. In this chapter, rough set-based clustering approaches are used
to discover Web user access patterns, and these techniques compute a number of clusters automatically
from the Web log data using statistical techniques. The suitability of rough clustering approaches for
Web page recommendation are measured using predictive accuracy metrics.
Chapter 10
Hybrid Neural Architecture for Intelligent Recommender System Classification Unit Design ........192
Emmanuel Buabin, Methodist University College Ghana, Ghana
The objective is intelligent recommender system classification unit design using hybrid neural tech-
niques. In particular, a neuroscience-based hybrid neural by Buabin (2011a) is introduced, explained,
and examined for its potential in real world text document classification on the modapte version ofthe
Reuters news text corpus. The so described neuroscience model (termed Hy-RNC) is fully integrated
with a novel boosting algorithm to augment text document classification purposes. Hy-RNC outperforms
existing works and opens up an entirely new research field in the area of machine learning. The main
contribution of this book chapter is the provision of a step-by-step approach to modeling the hybrid
system using underlying concepts such as boosting algorithms, recurrent neural networks, and hybrid
neural systems. Results attained in the experiments show impressive performance by the hybrid neural
classifier even with a minimal number of neurons in constituting structures.
Chapter 11
Newly Developed Nature-In spired Algorithms and their Applications to Recommendation
Systems ..............................................................................................................................................214
Utku Kose, Usak University, Turkey
Because of their mathematical backgrounds and coherent structures, Artificial Intelligent-based methods
and techniques are often used to find solutions for different types of problems encountered. In the related
context, nature-inspired algorithms are also important for providing more accurate solutions. Because
of their nature-based, flexible process structures, the related algorithms can be applied to different types
of problems. At this point, recommendation systems are one ofthe related problem areas where nature-
inspired algorithms can be used to get better results. In the literature there are many research studies
that are based on using nature-inspired algorithms within recommendation systems. This chapter aims
to discuss usage of some newly developed nature-inspired algorithms in typical recommendation sys-
tems. In this aim, features and functions of some new nature-inspired algorithms will be explained first.
Later, using the related algorithms in recommendation systems will be discussed. Following that, there
will be a discussion on future of nature-inspired algorithms and also their roles in the recommendation
approach or system-based applications.
Chapter 12
Modelling Clearance Sales Outshopping Behaviour Using Neural Network Model ........................230
M. Hemalatha, M.A.M. College of Engineering, India
The neural network is a very useful tool for approximation of a function, time series prediction, clas-
sification, and pattern recognition. If there is found to be a non-linear relationship between input data
and output data, it is difficult to analyse the system. A neural network is very effective to solve this
problem. This chapter studies the applied neural network model in relation to clearance sales outshop-
ping behaviour. Since neural network theory can be applied effectively to this case, the authors have
used neural network theory to recognise the retail area satisfaction and loyalty. To measure the impact
among the retail area attributes, retail area satisfaction, and retail area loyalty, the authors have used the
neural network model. In this chapter, they have treated twenty seven factors as the input signals into the
input layer. Therefore, they find the weights between nodes in the relationship between the value of all
twenty seven factors and the retail area satisfaction and loyalty. The development ofthe model by retail
area attributes, and their interpretation, was facilitated by a collection of data across three trading areas.
This neural network modeling approach to understand clearance sales outshopping behaviour provides
retail managers with information to support retail strategy development.
Chapter 13
Hybrid Neural Genetic Architecture: New Directions for Intelligent Recommender System
Design ................................................................................................................................................245
Emmanuel Buabin, Methodist University College Ghana, Ghana
The objective is a neural-based feature selection in intelligent recommender systems. In particular, a
hybrid neural genetic architecture is modeled based on human nature, interactions, and behaviour. The
main contribution of this chapter is the development of a novel genetic algorithm based on human nature,
interactions, and behaviour. The novel genetic algorithm termed Buabin Algorithm is fully integrated
with a hybrid neural classifier to form a Hybrid Neural Genetic Architecture. The research presents G A in
a more attractive manner and opens up the various departments of a G A for active research. Although no
scientific experiment is conducted to compare network performance with standard approaches, engaged
techniques reveal drastic reductions in genetic operator operations. For illustration purposes, the UCI
Molecular Biology (Splice Junction) dataset is used. Overall, Buabin Algorithm seeks to integrate
human related interactions into genetic algorithms as imitate human genetics in recommender systems
design and understand underlying datasets explicitly.
Chapter 14
Web Usage Mining Approaches for Web Page Recommendation: A Survey....................................271
H. Hannah Inbarani, Periyar University, India
K. Thangavel, Periyar University, India
The technology behind personalization or Web page recommendation has undergone tremendous changes,
and several Web-based personalization systems have been proposed in recent years. The main goal of
Web personalization is to dynamically recommend Web pages based on online behavior of users. Al-
though personalization can be accomplished in numerous ways, most Web personalization techniques
fall into four major categories: decision rule-based filtering, content-based filtering, and collaborative
filtering and Web usage mining. Decision rule-based filtering reviews users to obtain user demograph-
ics or static profiles, and then lets Web sites manually specify rules based on them. It delivers the ap-
propriate content to a particular user based on the rules. However, it is not particularly useful because
it depends on users knowing in advance the content that interests them. Content-based filtering relies
on items being similar to what a user has liked previously. Collaborative filtering, also called social or
group filtering, is the most successful personalization technology to date. Most successful recommender
systems on the Web typically use explicit user ratings of products or preferences to sort user profile in-
formation into peer groups. It then tells users what products they might want to buy by combining their
personal preferences with those of like-minded individuals. However, collaborative filtering has limited
use for a new product that no one has seen or rated, and content-based filtering to obtain user profiles
might miss novel or surprising information. Additionally, traditional Web personalization techniques,
including collaborative or content-based filtering, have other problems, such as reliance on subject user
ratings and static profiles or the inability to capture richer semantic relationships among Web objects.
To overcome these shortcomings, the new Web personalization tool, nonintrusive personalization, at-
tempts to increasingly incorporate Web usage mining techniques. Web usage mining can help improve
the scalability, accuracy, and flexibility of recommender systems. Thus, Web usage mining can reduce
the need for obtaining subjective user ratings or registration-based personal preferences. This chapter
provides a survey of Web usage mining approaches.
Compilation of References...............................................................................................................289
About the Contributors....................................................................................................................324
Index...................................................................................................................................................330
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| spelling | Intelligent techniques in recommendation systems contextual advancements and new methods [edited by] Satchidananda Dehuri 2013 xvii, 332 pages illustrations 29 cm txt rdacontent n rdamedia nc rdacarrier "Premier reference source"--Cover. Includes bibliographical references and index -- "This book is a comprehensive collection of research on the latest advancements of intelligence techniques and their application to recommendation systems and how they could improve this field of study"-- Provided by publisher. Decision support systems Entscheidungsunterstützungssystem (DE-588)4191815-0 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Entscheidungsunterstützungssystem (DE-588)4191815-0 s DE-604 Dehuri, Satchidananda 1976- Sonstige (DE-588)139541055 oth HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027491401&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Intelligent techniques in recommendation systems contextual advancements and new methods |
| title_auth | Intelligent techniques in recommendation systems contextual advancements and new methods |
| title_exact_search | Intelligent techniques in recommendation systems contextual advancements and new methods |
| title_full | Intelligent techniques in recommendation systems contextual advancements and new methods [edited by] Satchidananda Dehuri |
| title_fullStr | Intelligent techniques in recommendation systems contextual advancements and new methods [edited by] Satchidananda Dehuri |
| title_full_unstemmed | Intelligent techniques in recommendation systems contextual advancements and new methods [edited by] Satchidananda Dehuri |
| title_short | Intelligent techniques in recommendation systems |
| title_sort | intelligent techniques in recommendation systems contextual advancements and new methods |
| title_sub | contextual advancements and new methods |
| topic | Decision support systems Entscheidungsunterstützungssystem (DE-588)4191815-0 gnd |
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