Delivering a Great Digital Commerce Experience Optimizing Your Customer Path to Purchase Page 2 Ecommerce Guide Ecommerce Guide Page 3
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Rootkit- Rootkits.For.Dummies 2007.Pdf
01_917106 ffirs.qxp 12/21/06 12:04 AM Page i Rootkits FOR DUMmIES‰ 01_917106 ffirs.qxp 12/21/06 12:04 AM Page ii 01_917106 ffirs.qxp 12/21/06 12:04 AM Page iii Rootkits FOR DUMmIES‰ by Larry Stevenson and Nancy Altholz 01_917106 ffirs.qxp 12/21/06 12:04 AM Page iv Rootkits For Dummies® Published by Wiley Publishing, Inc. 111 River Street Hoboken, NJ 07030-5774 www.wiley.com Copyright © 2007 by Wiley Publishing, Inc., Indianapolis, Indiana Published by Wiley Publishing, Inc., Indianapolis, Indiana Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permit- ted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600. Requests to the Publisher for permission should be addressed to the Legal Department, Wiley Publishing, Inc., 10475 Crosspoint Blvd., Indianapolis, IN 46256, (317) 572-3447, fax (317) 572-4355, or online at http://www.wiley.com/go/permissions. Trademarks: Wiley, the Wiley Publishing logo, For Dummies, the Dummies Man logo, A Reference for the Rest of Us!, The Dummies Way, Dummies Daily, The Fun and Easy Way, Dummies.com, and related trade dress are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates in the United States and other countries, and may not be used without written permission. -
Personalized Recommendation System Based Association Rule Mining and Sentiment Classification
International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-9 Issue-4, April 2020 Personalized Recommendation System based Association Rule Mining and Sentiment Classification Senthil S Sekhar, K. Satyanarayana this issue. Online companies generally collect customer Abstract: In this emerging global economy, e-commerce is an reviews and use them to recommend products to other inevitable part of the business strategy. Moreover, the business companies at a lower cost. Such software systems that make world comprises the upcoming entrepreneurs who are unaware of customized responses are known as recommendation the current trends in marketing. Therefore, a recommendation systems. The recommendation systems are beneficial for system is very essential for them. In this paper, a fully automated both the user and the vendor. They save the time and money recommendation system for the upcoming entrepreneurs to become successful in their business is proposed. The system works of the users by helping them find out the best product and in three stages. In the first stage, the most transacted product is company within no time. However, the effectiveness and identified using association rule mining FP growth algorithm. reliability of such recommendation systems are to be studied This helps in extracting useful information from the previous properly. transacted data by mining the entire set of frequent patterns. The In this paper, a fully automated recommendation system is second stage identifies the most customer preferred company proposed. Here, association rule mining based on the FP based on review analysis. The multilevel clustering process with growth algorithm is used. The review data set thus collected the generalization of data review is implemented to achieve an is analyzed using a review analyzer system. -
Netbrain Integrated Edition Quick Start Guide
® NetBrain Integrated Edition 7.0 Quick Start Guide Version 7.0b1 | Last Updated 2017-11-03 Copyright ©2004-2017 NetBrain Technologies, Inc. All rights reserved. Contents 1. Introduction ................................................................................................................................................................................. 4 1.1. Signing Up ............................................................................................................................................................................. 4 1.2. Logging in to Your Domain ............................................................................................................................................... 5 1.3. Selecting a Domain ............................................................................................................................................................. 5 1.4. User Interface ...................................................................................................................................................................... 6 2. Documenting Your Network ..................................................................................................................................................... 7 2.1. Searching and Mapping ..................................................................................................................................................... 7 2.2. Extending Neighbors ......................................................................................................................................................... -
Crawling AJAX-Based Web Applications Through Dynamic Analysis of User Interface State Changes
Crawling AJAX-Based Web Applications through Dynamic Analysis of User Interface State Changes ALI MESBAH, University of British Columbia ARIE VAN DEURSEN and STEFAN LENSELINK, Delft University of Technology Using JAVASCRIPT and dynamic DOM manipulation on the client side of Web applications is becoming a widespread approach for achieving rich interactivity and responsiveness in modern Web applications. At the same time, such techniques—collectively known as AJAX—shatter the concept of webpages with unique URLs, on which traditional Web crawlers are based. This article describes a novel technique for crawl- ing AJAX-based applications through automatic dynamic analysis of user-interface-state changes in Web browsers. Our algorithm scans the DOM tree, spots candidate elements that are capable of changing the 3 state, fires events on those candidate elements, and incrementally infers a state machine that models the various navigational paths and states within an AJAX application. This inferred model can be used in pro- gram comprehension and in analysis and testing of dynamic Web states, for instance, or for generating a static version of the application. In this article, we discuss our sequential and concurrent AJAX crawling algorithms. We present our open source tool called CRAWLJAX, which implements the concepts and algo- rithms discussed in this article. Additionally, we report a number of empirical studies in which we apply our approach to a number of open-source and industrial Web applications and elaborate on the obtained results. Categories and Subject Descriptors: H.5.4 [Information Interfaces and Presentation]: Hypertext/ Hypermedia—Navigation; H.3.3 [Information Storage and Retrieval]: Information and Retrieval— Search process; D.2.2 [Software Engineering]: Design Tools and Techniques General Terms: Design, Algorithms, Experimentation Additional Key Words and Phrases: Crawling, Ajax, Web 2.0, hidden web, dynamic analysis, DOM crawling ACM Reference Format: Mesbah, A., van Deursen, A., and Lenselink, S. -
Your SEO TRAFFIC
Your SEO TRAFFIC Step up your SEO game with this 30-Day planner How to increase your SEO Traffic in 30 days RYTE & HubSpot 1 Introduction ........ 3 TABLE OF CONTENTS Conclusion .......... 19 1 2 3 4 5 6 Optimize Your Use ALT Identify & Standardize Review Your Shorten Your Title & Attributes to Rectify Your URL Redirects URLS Description Optimize Images Broken Links Structures Page 3 Page 4 Page 4 Page 5 Page 5 Page 6 7 9 Link Your 8 10 Improve 11 12 Do Your Use Anchor Keep Click Tell Search Pages Texts to Increase Your Site’s Keyword Paths Short Engines What Internally Relevance Accessibility to Crawl Research Page 6 Page 7 Page 7 Page 8 Page 9 Page 10 13 14 15 16 17 18 Ensure Neat Improve Optimize Identify Eliminate Use TF*IDF Website Your Site’s Your Website Duplicate Duplicate to Analyze Navigation Loading Speed for Mobile Content Content Content Page 11 Page 11 Page 12 Page 12 Page 13 Page 13 21 23 24 19 20 22 Keep Your Internationalize Create Unique Curate Your Recycle Your Content Code Diversify Your Website Headlines Content Content Ratio High New Content Page 14 Page 14 Page 15 Page 15 Page 16 Page 16 25 26 27 28 29 30 Optimize Your Leverage Sign Up for Sign Up Force User Monitor Website for Social Google Search for Google Interaction Your Progress Local Search Networks Console Analytics Page 17 Page 17 Page 18 Page 18 Page 19 Page 19 How to increase your SEO Traffic in 30 days RYTE & HubSpot 2 INTRODUCTION Day 1 OPTIMIZE YOUR TITLE AND DESCRIPTION Optimizing your website for search engines can be difficult, The first thing a user sees when they search for a keyword especially with all of the changing algorithms search engines is the snippet on the Google Search results page. -
Explaining Recommendations Based on Feature Sentiments in Product
IUI 2017 • Recommender Systems March 13–16, 2017, Limassol, Cyprus Explaining Recommendations Based on Feature Sentiments in Product Reviews Li Chen Feng Wang Department of Computer Science, Hong Kong Department of Computer Science, Hong Kong Baptist University Baptist University Hong Kong, China Hong Kong, China [email protected] [email protected] ABSTRACT serves to explain the recommendation process (i.e., the logic The explanation interface has been recognized important in of underlying algorithm) [16, 36, 2], or justify why the recom- recommender systems as it can help users evaluate recommen- mendation might be good for a user [37, 41, 15]. According to dations in a more informed way for deciding which ones are [14, 15, 39], the types and aims of explanation can be different relevant to their interests. In different decision environments, in different recommender systems. For instance, in collabo- the specific aim of explanation can be different. In high- rative filtering (CF) systems, the explanation can be in form investment product domains (e.g., digital cameras, laptops) of a histogram with grouping of neighbors in different rat- for which users usually attempt to avoid financial risk, how to ing categories (high, medium and low) for increasing system support users to construct stable preferences and make better transparency and persuasiveness [16]. Keywords or user-tags decisions is particularly crucial. In this paper, we propose based approaches can help users to make qualified and good a novel explanation interface that emphasizes explaining the decisions in CF or content-based recommender systems [37, tradeoff properties within a set of recommendations in terms of 41]. -
Generating Consumer Insights from Big Data
Association for Information Systems AIS Electronic Library (AISeL) Research Papers ECIS 2017 Proceedings Spring 6-10-2017 GENERATING CONSUMER INSIGHTS FROM BIG DATA CLICKSTREAM INFORMATION AND THE LINK WITH TRANSACTION-RELATED SHOPPING BEHAVIOR Daniel Schellong RWTH Aachen University, Aachen, Germany, [email protected] Jan Kemper RWTH Aachen University, Aachen, Germany, [email protected] Malte Brettel RWTH Aachen University, Aachen, Germany, [email protected] Follow this and additional works at: http://aisel.aisnet.org/ecis2017_rp Recommended Citation Schellong, Daniel; Kemper, Jan; and Brettel, Malte, (2017). "GENERATING CONSUMER INSIGHTS FROM BIG DATA CLICKSTREAM INFORMATION AND THE LINK WITH TRANSACTION-RELATED SHOPPING BEHAVIOR". In Proceedings of the 25th European Conference on Information Systems (ECIS), Guimarães, Portugal, June 5-10, 2017 (pp. -). ISBN 978-989-20-7655-3 Research Papers. http://aisel.aisnet.org/ecis2017_rp/24 This material is brought to you by the ECIS 2017 Proceedings at AIS Electronic Library (AISeL). It has been accepted for inclusion in Research Papers by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact [email protected]. GENERATING CONSUMER INSIGHTS FROM BIG DATA CLICKSTREAM INFORMATION AND THE LINK WITH TRANSACTION-RELATED SHOPPING BEHAVIOR Research Paper Schellong, Daniel, RWTH Aachen University, Aachen, Germany, [email protected] Kemper, Jan, RWTH Aachen University, Aachen, Germany, [email protected] Brettel, Malte, RWTH Aachen University, Aachen, Germany, [email protected] Abstract E-Commerce firms collect enormous amounts of information in their databases. Yet, only a fraction is used to improve business processes and decision-making, while many useful sources often remain un- derexplored. -
Introduction to E-Commerce Combining Business and Information Technology
MARTIN KÜTZ INTRODUCTION TO E-COMMERCE COMBINING BUSINESS AND INFORMATION TECHNOLOGY 2 Introduction to E-Commerce: Combining Business and Information Technology 1st edition © 2016 Martin Kütz & bookboon.com ISBN 978-87-403-1520-2 Peer review by Prof. Dr. Michael Brusch, Fachbereich 6, Hochschule Anhalt and Prof. Dr. Corinna V. Lang, Fachbereich 2, Hochschule Anhalt 3 INTRODUCTION TO E-COMMERCE CONTENTS CONTENTS Table of abbreviations 7 1 Basics and definitions 15 1.1 The term “E-Commerce” 16 1.2 Business models related to E-Commerce 24 1.3 Technical and economic challenges 34 1.4 Exercises 35 2 Frameworks and architectures 37 2.1 Actors and stakeholders 37 360° 2.2 Fundamental sales process 39 2.3 Technological elements 44 2.4 Exercises 360° 61 thinking. thinking. 360° thinking . 360° thinking. Discover the truth at www.deloitte.ca/careers Discover the truth at www.deloitte.ca/careers © Deloitte & Touche LLP and affiliated entities. Discover the truth at www.deloitte.ca/careers © Deloitte & Touche LLP and affiliated entities. © Deloitte & Touche LLP and affiliated entities. Discover the truth at www.deloitte.ca/careers 4 © Deloitte & Touche LLP and affiliated entities. INTRODUCTION TO E-COMMERCE CONTENTS 3 B2C business 62 3.1 The process model and its variants 62 3.2 The pricing challenge 77 3.3 The fulfilment challenge 79 3.4 The payment challenge 80 3.5 B2C-business and CRM 80 3.6 B2C software systems 81 3.7 Exercises 85 4 B2B business 86 4.1 The process model and its variants 86 4.2 B2B software systems 98 4.3 Exercises 106 5 Impact -
Clickstream Data Mining and Market Segmentation
How to cite: Gandhi, J., Dalal, K., & Ravani, H. (2018). Clickstream data mining and market segmentation. International Journal of Engineering & Computer Science, 1(1), 41-45. https://doi.org/10.31295/ijecs.v1n1.22 Clickstream Data Mining and Market Segmentation Jay Gandhi Huntington Park, California, United States. [email protected] Kushal Dalal Huntington Park, California, United States Hitesh Ravani Huntington Park, California, United States Abstract Clickstream data is one of the most important sources of information in websites usage and customers' behavior in Banks e-services. A number of web usage mining scenarios are possible depending on the available information. While simple traffic analysis based on clickstream data may easily be performed to improve the e-banks services. The banks need data mining techniques to substantially improve Banks e-services activities. The relationships between data mining techniques and the Web usage mining are studied. Web structure mining has three types of these types are web usage structure, mining data streams, and web content. The integration between the Web usage mining and data mining techniques are presented for processes at different stages, including the pattern discovery phases, and introduces banks cases, that have analytical mining technique. A general framework for fully integrating domain Web usage mining and data mining techniques are represented for processes at different stages. Data Mining techniques can be very helpful to the banks for better performance, acquiring new customers, fraud detection in real time, providing segment based products, and analysis of the customers purchase patterns over time. And in Market Segmentation the importance of data mining techniques for market segmentation is becoming indispensable in the field of marketing research. -
Web Analytics from Wikipedia, the Free Encyclopedia
http://en.wikipedia.org/wiki/Web_analytics Web analytics From Wikipedia, the free encyclopedia Web analytics is the measurement, collection, analysis and reporting of internet data for purposes of understanding [1] and optimizing web usage. Web analytics is not just a tool for measuring web traffic but can be used as a tool for business and market research, and to assess and improve the effectiveness of a web site. Web analytics applications can also help companies measure the results of traditional print or broadcast advertising campaigns. It helps one to estimate how traffic to a website changes after the launch of a new advertising campaign. Web analytics provides information about the number of visitors to a website and the number of page views. It helps gauge traffic and popularity trends which is useful for market research. There are two categories of web analytics; off-site and on-site web analytics. Off-site web analytics refers to web measurement and analysis regardless of whether you own or maintain a website. It includes the measurement of a website's potential audience (opportunity), share of voice (visibility), and buzz (comments) that is happening on the Internet as a whole. On-site web analytics measure a visitor's behavior once on your website. This includes its drivers and conversions; for example, the degree to which different landing pages are associated with online purchases. On-site web analytics measures the performance of your website in a commercial context. This data is typically compared against key performance indicators for performance, and used to improve a web site or marketing campaign's audience response. -
Product Recommendations in E-Commerce Retailing Applications
FORSCHUNGSERGEBNISSE DER WIRTSCHAFTSUNIVERSITÄT WIEN Nicolas Knotzer Product Recommendations in E-Commerce Retailing Applications Nicolas Knotzer - 978-3-631-75452-8 Downloaded from PubFactory at 01/11/2019 04:40:47AM via free access FORSCHUNGSERGEBNISSE DER WIRTSCHAFTSUNIVERSITÄT WIEN Nicolas Knotzer Product Recommendations in E-Commerce Retailing Applications The book deals with product recommendations generated by information systems referred to as recommender systems. Recommender systems assist consumers in making product choices by providing recommendations of the range of products and services offered in an online purchase environment. The quantitative research study investigates the influence of psychographic and sociodemographic determinants on the interest of consumers in personalized online book recommendations. The author presents new findings regarding the interest in recommendations, importance of product reviews for the decision process, motives for submitting ratings as well as comments, and the delivery of recommendations. The results show that opinion seeking, opinion leading, domain specific innovativeness, online shopping experience, and age are important factors in respect of the interest in personalized recommendations. Nicolas Knotzer studied business administration with the focus on information systems, management control and project management. From 2001 to 2006 he joined the Institute for Management Information Systems at the Vienna University of Economics and Business Administration. The author received his doctoral -
User Modeling of Online Consumers
Association for Information Systems AIS Electronic Library (AISeL) ECIS 2013 Completed Research ECIS 2013 Proceedings 7-1-2013 User Modeling Of Online Consumers: Between- Gender Differences In Click Path Data Markus Weinmann Braunschweig Institute of Technology, Braunschweig, Germany, [email protected] Christoph Schneider City University of Hong Kong, Hong Kong, China, [email protected] Susanne Robra-Bissantz Braunschweig Institute of Technology, Braunschweig, Germany, [email protected] Follow this and additional works at: http://aisel.aisnet.org/ecis2013_cr Recommended Citation Weinmann, Markus; Schneider, Christoph; and Robra-Bissantz, Susanne, "User Modeling Of Online Consumers: Between-Gender Differences In Click Path Data" (2013). ECIS 2013 Completed Research. 188. http://aisel.aisnet.org/ecis2013_cr/188 This material is brought to you by the ECIS 2013 Proceedings at AIS Electronic Library (AISeL). It has been accepted for inclusion in ECIS 2013 Completed Research by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact [email protected]. Proceedings of the 21st European Conference on Information Systems USER MODELING OF ONLINE CONSUMERS: BETWEEN- GENDER DIFFERENCES IN CLICK PATH DATA Weinmann, Markus, Braunschweig Institute of Technology, Mühlenpfordtstraße 23, 38106 Braunschweig, Germany, [email protected] Schneider, Christoph, City University of Hong Kong, Kowloon, Hong Kong, [email protected] Robra-Bissantz, Susanne, Braunschweig Institute of Technology, Mühlenpfordtstraße 23, 38106 Braunschweig, Germany, [email protected] Abstract Given people’s differences in preferences, personalities, or demographic factors, building user- adaptive systems can aid in providing each individual user with the interface that helps to best achieve the desired outcome.