Personalized Content Recommendation Engine for Web Publishing Services Using Textmining and Predictive Analytics

Recommendation systems have become very important especially for internet based business such as e-commerce and web publishing. While content based filtering and collaborative filtering are most commonly used groups in recommendation systems there are still researches for new approaches. In this study, a personalized recommendation system based on text mining and predictive analytics is proposed for a real world web publishing company. The approach given in this chapter first preprocesses existing web contents, integrate the structured data with history of a specific user and create an extended TDM for the user. Then this data is used for prediction of the users interest in new content. In order to reach that point, SVM, K-NN and Naïve Bayesian methods are used. Finally, the best performing method is used for determining the interest level of the user in a new content. Based on the forecasted interest levels the system recommends among the alternatives.

Descriptive Analytics

Data analytics has becoming one of the challenging platforms across various domains such as telecom, health care, social media and so on. The challenging and most promising task in analytics is the understanding of various patterns in the data. The mechanism of data retrieval and analysis seems to be the promising one in which the algorithms, techniques, way of processing data are in need with the ability to target upon large volumes of data. There are various types of analytical methods such as predictive analytics, descriptive analytics, text analytics, social media analytics and survival analytics. This chapter mainly focuses towards the mechanism of descriptive analytics its types, algorithms and applications. There are various forms of tools and techniques such as association rule mining, sequence rule mining, and data categorization such as hierarchical and non-hierarchical clustering methods with its variants.

Alternative Clustering

In the last couple of decades, clustering has become a very crucial research problem in the data mining research community. Clustering refers to the partitioning of data objects such as records and documents into groups or clusters of similar characteristics. Clustering is unsupervised learning, because of unsupervised nature there is no unique solution for all problems. Most of the time complex data sets require explanation in multiple clustering sets. All the Traditional clustering approaches generate single clustering. There is more than one pattern in a dataset; each of patterns can be interesting in from different perspectives. Alternative clustering intends to find all unlike groupings of the data set such that each grouping has high quality and distinct from each other. This chapter gives you an overall view of alternative clustering; it's various approaches, related work, comparing with various confusing related terms like subspace, multi-view, and ensemble clustering, applications, issues, and challenges.

Prioritizing and Analyzing Demand Chain Management (DCM) Processes in Indian Retailing Using AHP

The extant body of literature on demand chain management (DCM) is predominantly conceptual and unequivocal on how to implement it in a real business setting. Keeping the research gap into account, the study aims to identify and prioritize the DCM processes or variables in the context of Indian retailing. The data were collected using survey method using a structured questionnaire with Saaty (1980) scale. The method employed for analyzing the collected data was analytical hierarchy process or AHP. The results of the study have interesting implications for the industry vis-à-vis literature. Some quick measures revealed that the processes which are critical to implementation of DCM in retail context, in the order of importance, comprises supplier relationship management, customer relationship management assortment planning, top-management commitment and support, marketing orientation, information management, supply chain leagility, customer service management, category management, purchasing management, inventory management, and category tactics.

Predictive Analysis of Emotions for Improving Customer Services

Human emotions plays an important role in everyday communication. Emotions are formed by the combination of cues such as relative actions, facial expressions, and gestures and reactions. Emotions are also present in written texts like in social media, chats, customer reviews. By getting inspired by works done in the domain of sentiment analysis, this chapter explores advances to automatic detection of emotions in text which help in Improving Customer Services. This chapter presents a framework for automatic detection of emotions in customer reviews based on different emotions theories in the fields of psychology and linguistics. This framework uses advanced Machine Learning (ML) techniques with Natural Language Processing (NLP) methods for better understanding of emotion detection and recognition in customer reviews. The text under study comprises data collected from leading Indian e-commerce portals like Flipkart, Snapdeal and Amazon, which contains text rich in emotions. The advantages and application based emotion detection framework has been incorporated with suitable examples.

Bug Handling in Service Sector Software

The technology enabled service industry is emerging as the most dynamic sectors in world's economy. Various service sector industries such as financial services, banking solutions, telecommunication, investment management, etc. completely rely on using large scale software for their smooth operations. Any malwares or bugs in these software is an issue of big concern and can have serious financial consequences. This chapter addresses the problem of bug handling in service sector software. Predictive analysis is a helpful technique for keeping software systems error free. Existing research in bug handling focus on various predictive analysis techniques such as data mining, machine learning, information retrieval, optimisation, etc. for bug resolving. This chapter provides a detailed analysis of bug handling in large service sector software. The main emphasis of this chapter is to discuss research involved in applying predictive analysis for bug handling. The chapter also presents some possible future research directions in bug resolving using mathematical optimisation techniques.

Analytics in Public Policy Related to Service Sector

Big Data is transforming industries such as healthcare, financial services and banking, insurance, pharmacy, and telecommunication. Big Data concerns datasets that are not only big, but also high in variety and velocity, which makes them difficult to manage applying traditional tools and techniques. Big Data causes multitude benefits and advantages for industries such as marketing and selling, fraud detection, competitive advantage, risk reduction, and finally decision making and policy making. Due to the rapid growth of such data, methodologies and conceptual architectures need to be studied and provided in order to handle and extract value and knowledge from these data. The purpose of this chapter is studying Big Data benefits, characteristics, methodologies, and conceptual architectures in five different industries. Finally, according to the studies, a comprehensive methodology and architecture are proposed which might be applicable in service sector and one of the useful outcomes can be public policies.

Vehicular Traffic Forecasting in Filling Station

Forecasting is the one of the important part of decision making process. It helps managers to identify short term and long term future trends in the business activities. It may help in forecasting demand in retail store, predicting customer traffic at the petrol pump, calculation of probable population in upcoming years etc. There are plenty of studies published on forecasting techniques which are just introductory or highly mathematical and lacks in providing managerial perspective of solving business problems to the students. This chapter elucidates various forecasting techniques and its application in the field of management. In addition, various examples of real life problems are solved and analyzed with multiple forecasting techniques. Through this chapter students will have a clear understanding of the various nuances of different forecasting models in one single data set. Students will be able to identify future trend and seasonality in real life data set and evaluate more appropriate forecasting technique for the decision-making process.

Strengths and Limitations of Social Media Analytics Tools

The aim of this chapter is to research and present strengths and limitations of social media analytics tools used in the financial sector. Emphasis is on the business point of view that sees the social media analytics as a collection of tools that transform semi-structured and unstructured social data into noteworthy business insight. There are two main aspects of social media analytics: the technology aspect which covers identifying, extracting, and analyzing social media data using sophisticated tools and techniques; and the business aspect which interprets the data findings and aligns them with business goals. Namely, it is simply not enough to have a social media analytics tool; the tool should be strategically aligned to support existing business goals. The chapter offers a framework for easier adoption and implementation of these tools in the financial sector.

Recommender System

With the increase in user choices and rapid change in user preferences, various methods required to capture such increasing choices and changing preferences. Online systems require quick adaptability. Another aspect is that with the increase in a number of items and users, computation time increases considerably. Thus system needs parallel computing platform to run newer designed recommender system techniques. Recommendation system helps people to tackle the choice overload problem and help to select the efficient one. Even though there is lots of work have been done in the recommendation system, still there is a problem in handling various types of data and basically to handle a large amount of data. The main aim of the recommendation system is to provide the best opinion from the available large amount of data. The present chapter describes an introduction to recommender systems, its functions, types, techniques, applications, collaborative filtering, content-based filtering and evaluation of performance.