A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce Author ： Rustam Vahidov, Fei Ji Source ： Electrnic Commerce Research and.

Slides:

Advertisements

Similar presentations

Study On Intelligent E-Shopping System Based On Data Mining

Advertisements

© Telelogic AB Trade Studies with Focal Point ®. © Telelogic AB Discuss Trade Studies on the design of a JTRS Radio Design.

Psychological Advertising: Exploring User Psychology for Click Prediction in Sponsored Search Date: 2014/03/25 Author: Taifeng Wang, Jiang Bian, Shusen.

Intelligent Database Systems Lab Presenter : WU, MIN-CONG Authors : KADIM TA¸SDEMIR, PAVEL MILENOV, AND BROOKE TAPSALL 2011,IEEE Topology-Based Hierarchical.

Smart Shopper A Consumer Decision Support System Using Type-2 Fuzzy Logic Systems Ling Gu 2003 Fall CSc8810.

EMBA Projects. Introduction EMBA Project is mandatory requirement for degree completion Two projects One project with 2 courses instead of second project.

Back to Table of Contents

By Srishti Gahlot (sg2856) 1. 2 What do you mean by online behavior? Why do we need to analyze online behavior and personalize it? How do we analyze this.

5-1. McGraw-Hill/Irwin Copyright © 2008 by The McGraw-Hill Companies, Inc. All Rights Reserved. Customer Analysis Chapter 05.

Explorations in Tag Suggestion and Query Expansion Jian Wang and Brian D. Davison Lehigh University, USA SSM 2008 (Workshop on Search in Social Media)

Recommender Systems Aalap Kohojkar Yang Liu Zhan Shi March 31, 2008.

VLab Web Application Architecture Xinyi Dong 10/1/

Video Table-of-Contents: Construction and Matching Master of Philosophy 3 rd Term Presentation - Presented by Ng Chung Wing.

WebMiningResearch ASurvey Web Mining Research: A Survey Raymond Kosala and Hendrik Blockeel ACM SIGKDD, July 2000 Presented by Shan Huang, 4/24/2007.

ADVISE: Advanced Digital Video Information Segmentation Engine

WebMiningResearchASurvey Web Mining Research: A Survey Raymond Kosala and Hendrik Blockeel ACM SIGKDD, July 2000 Presented by Shan Huang, 4/24/2007 Revised.

Sparsity, Scalability and Distribution in Recommender Systems

Collaborative Filtering CMSC498K Survey Paper Presented by Hyoungtae Cho.

8 Systems Analysis and Design in a Changing World, Fifth Edition.

Ensuring Home-based Rehabilitation Exercise by Using Kinect and Fuzzified Dynamic Time Warping Algorithm Qiao Zhang.

A Smart B2C e-Commerce System Based on ACP Approach Chen Lyu, Lefei Li, and Tao Pan, Tsinghua University Intelligent Systems, IEEE, vol. 29, issue. 4,

Recommender Systems on the Web: A Model-Driven Approach Gonzalo Rojas – Francisco Domínguez – Stefano Salvatori Department of Computer Science University.

Chapter 21 Copyright ©2012 by Cengage Learning Inc. All rights reserved 1 Lamb, Hair, McDaniel CHAPTER 21 Customer Relationship Management (CRM)

2008/06/06 Y.H.Chang Towards Effective Browsing of Large Scale Social Annotations1 Towards Effective Browsing of Large Scale Social Annotations WWW 2007.

A Recommendation Mechanism for Contextualized mobile advertising S.-T. Yuan et al., Expert Systems with Applications, vol. 24, no. 4, pp , 2003.

Tag Clouds Revisited Date : 2011/12/12 Source : CIKM’11 Speaker : I- Chih Chiu Advisor : Dr. Koh. Jia-ling 1.

Item Based Collaborative Filtering Recommendation Algorithms Badrul Sarwar, George Karpis, Joseph KonStan, John Riedl (UMN) p.s.: slides adapted from:

Marketing Marketing Basics

Cooperative Meeting Scheduling among Agents based on Multiple Negotiations Toramatsu SHINTANI and Takayuki ITO Department of Intelligence and Computer.

Sunilkumar S. Manvi and P. Venkataram Protocol Engineering and Technology Unit, ECE Dept. Indian Institute of Science Bangalore, , INDIA

南台科技大學資訊工程系 A web page usage prediction scheme using sequence indexing and clustering techniques Adviser: Yu-Chiang Li Speaker: Gung-Shian Lin Date:2010/10/15.

CIS 600: Master's Project Online Trading and Data Mining- Based Marketing of IT Books Supervisor : Dr. Haiping Xu Student : Tsung-Ta Tu Student ID :

Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 The k-means range algorithm for personalized data clustering.

IMPROVING E-COMMERCE COLLABORATIVE RECOMMENDATIONS BY SEMANTIC INFERENCE OF NEIGHBORS’ PRACTICAL EXPERTISE 6 th International Workshop on Semantic Media.

Introduction Mobile commerce(M-Commerce) is developing trend due to successful experience of E-Commerce(EC) Most significant difference between M-Commerce.

Log files presented to : Sir Adnan presented by: SHAH RUKH.

Learning Geographical Preferences for Point-of-Interest Recommendation Author(s): Bin Liu Yanjie Fu, Zijun Yao, Hui Xiong [KDD-2013]

HK International Tea Fair 2009 On-site Survey Christine Kwok Managing Consultant Actrium Solutions 17 August 2009.

You Are What You Tag Yi-Ching Huang and Chia-Chuan Hung and Jane Yung-jen Hsu Department of Computer Science and Information Engineering Graduate Institute.

Job scheduling algorithm based on Berger model in cloud environment Advances in Engineering Software (2011) Baomin Xu,Chunyan Zhao,Enzhao Hua,Bin Hu 2013/1/251.

1 Cluster Analysis Objectives ADDRESS HETEROGENEITY Combine observations into groups or clusters such that groups formed are homogeneous (similar) within.

Templated Search over Relational Databases Date: 2015/01/15 Author: Anastasios Zouzias, Michail Vlachos, Vagelis Hristidis Source: ACM CIKM’14 Advisor:

1 Introduction to Business and Economics Copyright Goodheart-Willcox Co., Inc. May not be posted to a publicly accessible website. Section 1.1 Introduction.

Auto Diagnosing: An Intelligent Assessment System Based on Bayesian Networks IEEE 2007 Frontiers In Education Conference- Global Engineering : Knowledge.

HNC Computing (Systems Support), Castle College Systems Analysis Lecture 1 Introduction How business use IS Role of Analyst.

Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Utilizing Marginal Net Utility for Recommendation in E-commerce.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Fraud detection in online consumer reviews Presenter: Tsai Tzung Ruei Authors: Nan Hu, Ling Liu, Vallabh.

Consumer Behavior and Loyalty. Electronic CommercePrentice Hall © Learning about Consumer Behavior Online A Model of Consumer Behavior Online –The.

Cluster Analysis.

CYUT Information Management Inference of Recommendation Information on the Internet Using Improved FAM Authors:Won Kim, Il-Ju Ko, Jin-Sung Yoon, Gye-Young.

Authors: Soamsiri Chantaraskul, Klaus Moessner Source: IET Commun., Vol.4, No.5, 2010, pp Presenter: Ya-Ping Hu Date: 2011/12/23 Implementation.

Clustering Algorithms Sunida Ratanothayanon. What is Clustering?

MKT 346: Marketing of Services Dr. Houston Chapter 3: Positioning Services In Competitive Markets.

CYUT ISKM 2004/01/13 1 Fuzzy logic methods in recommender systems Author: Ronald R. Yager Source:Fuzzy set and systems, Vol. 134, 2003, pp Presented.

Master thesis spring Anders Gimmestad Gule Calendars as User Context Providers in an E-learning Environment Supervisor: Rune Hjelsvold Apropos-Internett:

E-commerce in Your Inbox Product Recommendations at Scale

Universitatea Politehnica Bucureşti - Facultatea de Automatică şi Calculatoare Towards a Peer-to-Peer Recommender System Based on Collaborative Filtering.

1 A methodology for dynamic data mining based on fuzzy clustering Source: Fuzzy Sets and Systems Volume: 150, Issue: 2, March 1, 2005, pp Authors:

A Recommender System based on Tag and Time Information for Social Tagging Systems Nan Zheng and Qiudan Li (Chinese Academy of Sciences) Expert Systems.

Type-2 Fuzzy Web Shopping Agents Menglei Tang and Yanqing Zhang Georgia State University Gang Zhang Tianjin University.

GUILLOU Frederic. Outline Introduction Motivations The basic recommendation system First phase : semantic similarities Second phase : communities Application.

Client-Server Movie Service Charles Snyder. Concept  Media recommendation service  Movie database  Categorized by genre relevance  Users have some.

Mining Utility Functions based on user ratings

Chapter 13 Marketing: Helping Buyers Buy

Centrality Bias Measure for High Density QR Code Module Recognition

Web Mining Department of Computer Science and Engg.

Date: 2012/11/15 Author: Jin Young Kim, Kevyn Collins-Thompson,

Color Image Retrieval based on Primitives of Color Moments

WSExpress: A QoS-Aware Search Engine for Web Services

Color Image Retrieval based on Primitives of Color Moments

Presentation transcript:

A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce Author ： Rustam Vahidov, Fei Ji Source ： Electrnic Commerce Research and Applications Vol.4, 2005, pp Presented By ： Ya-Ju Liu ( 劉雅茹 ) Date ： 2005/12/15

2005/12/15 A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce 2 Outline Introduction Methodology Fuzzy Weighted-Sum Model Cluster Analysis Architecture Conclusion Comment

2005/12/15 A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce 3 Introduction Motivation The wealth of information threatens to overload the customers Purpose Propose a method for supporting consumer buying decisions in EC

2005/12/15 A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce 4 Methodology Fuzzy weighted-sum model (FWSM)FWSM Partition the alternatives into different grade Cluster analysis (CA)CA Explore similarity between people, products, and behaviors Provide the most dissimilar cluster

2005/12/15 A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce 5 FWSM ： Fuzzy utility ： Utility of the i th attribute of product ： Fuzzy weight of that attribute Calculate the attractiveness of product

2005/12/15 A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce 6 CA Group observations based on the similarity measure X, Y ： products w i ： weight i ： product features

2005/12/15 A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce 7 Architecture User Interface (WWW) Criteria Management Fuzzy Filter Product DB Clustering Module Recommendations Generator Recommendations Profiles Buyer Client Server

2005/12/15 A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce 8 Criteria Management Partition the products into different classes Fuzzy terms  Fuzzy weightsFuzzy terms  Fuzzy weights Provide the most dissimilar clusters Cluster analysis (CA) Euclidian distance metric

2005/12/15 A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce 9 Fuzzy Weight

2005/12/15 A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce 10 Fuzzy Filter Assign grades to the products Fuzzy utility

2005/12/15 A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce 11 Fuzzy Utility (1/2) 0.75* * * *1+1*1+1*1+0*1+0* *1 = * * * *4+1*4+1*4+0*4+0* *4 = * * * *7+1*7+1*7+0*7+0* *7 = 35.36

2005/12/15 A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce 12 Fuzzy Utility (2/2)

2005/12/15 A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce 13 Clustering Module Clustered using hierarchical method N diverse alternatives are presented to the user Example ： According to the weights (price) N=3 Three alternatives would greatly vary on price and be presented

2005/12/15 A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce 14 Recommender Profile Generated by the system in terms of key types Match between the alternatives and the profiles Luxury Value Budget

2005/12/15 A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce 15 Recommendation List

2005/12/15 A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce 16 Conclusion Fuzzy model Allow customers some level of latitude in describing their preferences Diverse product recommendations would help the customers to find the right products The method has to be effectively employed by the EC websites

2005/12/15 A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce 17 Comment Fuzzy method using in recommender system Crisp Set  Fuzzy Set

2005/12/15 A Diversity-based Method for Infrequent Purchase Decision Support in E-Commerce 18 Thanks for your listening