Personalization Technologies: A Process-Oriented Perspective Communications of the ACM (October 2005) Presented By Gediminas Adomavicius, Alexander Tuzhilin.

Slides:



Advertisements
Similar presentations
1 ©2009 MeeMix MeeMix – A personalized Experience.
Advertisements

Opportunities for the Use of Recommendation and Personalization Algorithms in meLearning Environments Tom E. Vandenbosch World Agroforestry Centre (ICRAF)
Recommender Systems & Collaborative Filtering
Multi-Channel Retailing. Multi-channel Retailing in 2005, U.S. online consumers will spend in excess of $632 billion (US$) in offline channels as a direct.
Back to Table of Contents
Experiments on Query Expansion for Internet Yellow Page Services Using Log Mining Summarized by Dongmin Shin Presented by Dongmin Shin User Log Analysis.
Chapter 21 Copyright ©2012 by Cengage Learning Inc. All rights reserved 1 21 Customer Relationship Management (CRM) Professor Close.
Recommender Systems – An Introduction Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich Cambridge University Press Which digital.
The Experience Factory May 2004 Leonardo Vaccaro.
Recommender Systems Aalap Kohojkar Yang Liu Zhan Shi March 31, 2008.
Artificial Intelligence and Case-Based Reasoning Computer Science and Engineering Mälardalen University Västerås, Mikael Sollenborn, CSL,
© Prentice Hall1 DATA MINING TECHNIQUES Introductory and Advanced Topics Eamonn Keogh (some slides adapted from) Margaret Dunham Dr. M.H.Dunham, Data Mining,
Agent Technology for e-Commerce
Recommender systems Ram Akella February 23, 2011 Lecture 6b, i290 & 280I University of California at Berkeley Silicon Valley Center/SC.
Recommender Systems; Social Information Filtering.
Recommender systems Ram Akella November 26 th 2008.
2006/12/191 Using E-CRM for a unified view of the customer COMMUNICATIONS OF THE ACM, April 2003, Vol.46 No.4 Shan L. Pan & Jae-Nam Lee Reporter: Shing-Jiun.
Personalization in e-Commerce Dr. Alexandra Cristea
Introduction to Systems Analysis and Design
FALL 2012 DSCI5240 Graduate Presentation By Xxxxxxx.
Game Theory and Privacy Preservation in Recommendation Systems Iordanis Koutsopoulos U of Thessaly Thalis project CROWN Kick-off Meeting Volos, May 11,
An Intelligent Broker Architecture for Context-Aware Systems A PhD. Dissertation Proposal in Computer Science at the University of Maryland Baltimore County.
STRATEGIES FOR ONLINE LEARNING IN A GLOBAL NETWORK UNIVERSITY INTED 2013 Annette Smith, Kristopher Moore, Erica Osher Reifer New York University.
Chapter 19Copyright ©2008 by South-Western, a division of Thomson Learning. All rights reserved 1 MKTG Designed by Amy McGuire, B-books, Ltd. Prepared.
1 Chapter 21: Customer Relationship Management (CRM) Prepared by Amit Shah, Frostburg State University Designed by Eric Brengle, B-books, Ltd. Copyright.
Chapter 21 Copyright ©2012 by Cengage Learning Inc. All rights reserved 1 Lamb, Hair, McDaniel CHAPTER 21 Customer Relationship Management (CRM)
Enabling Organization-Decision Making
WELCOME TO UNIT 7 Customer Service MT 221 Marilyn Radu, Instructor.
Customer Relationship Management Key Concepts. Customer Relationship Management Strategy Link all processes of the company from its customers through.
1Chap. 20 Marketing 7e Lamb Hair McDaniel ©2004 South-Western/Thomson Learning Prepared by Deborah Baker Texas Christian University Chapter 20 Customer.
1.Understand the essential elements that comprise a customer relationship management program 2.Describe the relationship that exists between marketing.
Recommender systems Drew Culbert IST /12/02.
Improving Customer Experience through Personalization Ron Owens Wednesday, August 9, 2006.
Chapter 12 Copyright ©2012 by Cengage Learning Inc. All rights reserved 1 Lamb, Hair, McDaniel CHAPTER 21 Customer Relationship Management (CRM)
Sarah Fatima Varda Sarfraz.  What is Recommendation systems?  Three recommendation approaches  Content-based  Collaborative  Hybrid approach  Conclusions.
A Hybrid Recommender System: User Profiling from Keywords and Ratings Ana Stanescu, Swapnil Nagar, Doina Caragea 2013 IEEE/WIC/ACM International Conferences.
Chapter 19Copyright ©2008 by South-Western, a division of Thomson Learning. All rights reserved 1 MKTG Designed by Amy McGuire, B-books, Ltd. Prepared.
Introduction To System Analysis and Design
Chapter 19Copyright ©2009 Cengage Learning Inc. All rights reserved 1 MKTG Designed by Amy McGuire, B-books, Ltd. Prepared by Deborah Baker, Texas Christian.
1 Customer Segmentation in Self Service Linda Van Doren Vanguard Communications Corporation August 9, 2006.
BestChoice: A Decision Support System for Supplier Selection in e-Marketplaces June 26, 2006 Dongjoo Lee, Tahee Lee, Sue-kyung Lee, Ok-ran Jeong, Hyeonsang.
Toward the Next generation of Recommender systems
1 Business System Analysis & Decision Making – Data Mining and Web Mining Zhangxi Lin ISQS 5340 Summer II 2006.
Collaborative Information Retrieval - Collaborative Filtering systems - Recommender systems - Information Filtering Why do we need CIR? - IR system augmentation.
Objectives Objectives Recommendz: A Multi-feature Recommendation System Matthew Garden, Gregory Dudek, Center for Intelligent Machines, McGill University.
Internet Marketing Customer Support and Online Quality.
CoOL: A Context Ontology Language to Enable Contextual Interoperability Thomas Strang, Claudia Linnhoff-Popien, and Korbinian Frank German Aerospace Centor.
Recommender Systems. Recommender Systems (RSs) n RSs are software tools providing suggestions for items to be of use to users, such as what items to buy,
User Modeling and Recommender Systems: Introduction to recommender systems Adolfo Ruiz Calleja 06/09/2014.
Bloom Cookies: Web Search Personalization without User Tracking Authors: Nitesh Mor, Oriana Riva, Suman Nath, and John Kubiatowicz Presented by Ben Summers.
Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.
Copyright © Houghton Mifflin Company. All rights reserved. 4–1 Chapter Outline Marketing on the Internet –Basic Characteristics of Electronic Marketing.
Trying to improve editing tasks through EDR methods Pedro Revilla, Ignacio Arbués, Margarita Gonzalez and Isabel Yun National Statistical Institute, Spain.
Descriptive Research & Questionnaire Design. Descriptive Research Survey versus Observation  Survey Primary data collection method based on communication.
GAS ontology: an ontology for collaboration among ubiquitous computing devices International Journal of Human-Computer Studies (May 2005) Presented By.
Item-Based Collaborative Filtering Recommendation Algorithms Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl GroupLens Research Group/ Army.
WELCOME TO UNIT 7. Unit 7 The Impact of Globalization on Customer Service Objectives Understand the impact globalization has had on the world economy.
Chapter 12 Extending the Organization to Customers.
Collaborative Filtering - Pooja Hegde. The Problem : OVERLOAD Too much stuff!!!! Too many books! Too many journals! Too many movies! Too much content!
Data Resource Management – MGMT An overview of where we are right now SQL Developer OLAP CUBE 1 Sales Cube Data Warehouse Denormalized Historical.
Customer Relationship Management. Presentation By: Tarun Rattan Jyoti Sodani Akash Gupta Saloni.
Recommender Systems & Collaborative Filtering
Chapter 21: Customer Relationship Management (CRM)
Object-Oriented Software Engineering Using UML, Patterns, and Java,
19 MKTG CHAPTER Lamb, Hair, McDaniel
Preface to the special issue on context-aware recommender systems
Presentation transcript:

Personalization Technologies: A Process-Oriented Perspective Communications of the ACM (October 2005) Presented By Gediminas Adomavicius, Alexander Tuzhilin Information and Decision Sciences, Carlson School of Management, University of Minnesota Information, Operation and Management Sciences, Stern School of Business, New York University Summerized By Jaeseok Myung

Copyright  2008 by CEBT Outline  Introduction Definitions Personalization Engine  Personalization Process Understand-Deliver-Measure Cycle Data Collection Build Customer Profile Matchmaking Delivery & Presentation Measuring Personalization Impact Adjusting Personalization Strategy  Future Work on Personalization Process Center for E-Business Technology

Copyright  2008 by CEBT Definitions  Personalization is the ability to provide content and services that are tailored to individuals based on knowledge about their preferences and behavior [“Smart Personalization”, Forrester Report, 1999]  Personalization is the combined use of technology and customer information to tailor electronic commerce interactions between a business and each individual customer. Using information either previously obtained or provided in real-time about the customer and other customers, the exchange between the parties is altered to fit that customer’s stated needs so that the transaction requires less time and delivers a product best suited to that customer [  Personalization is the capability to customize communication based on knowledge preferences and behaviors at the time of interaction [CRM Handbook, 2002]  These definitions state collectively that Tailors certain offerings By providers to the consumers Based on knowledge about them with certain goal in mind Center for E-Business Technology

Copyright  2008 by CEBT Personalization Engine  Personalized offers can be delivered from providers to consumers by personalization engines in three ways Center for E-Business Technology

Copyright  2008 by CEBT Personalization Process - (1)  Personalization constitutes an iterative process that can be defined by the Understand-Deliver-Measure cycle Understand consumers by collecting comprehensive information about them and converting it into actionable knowledge stored in consumer profiles Deliver personalized offering based on the knowledge about each consumer, as stored in the consumer profile Measure personalization impact by determining how much the consumer is satisfied with the delivered personalized offering Center for E-Business Technology

Copyright  2008 by CEBT Personalization Process – (2)  The technical implementation of the Understand-Deliver-Measure cycle consists of the six stages Center for E-Business Technology key issues

Copyright  2008 by CEBT Personalization Process – (3) 1. Data Collection The objective is to obtain the most comprehensive ‘picture’ of a consumer Various and heterogeneous data sources (Web, phone, mail,..) Can be solicited explicitly or tracked implicitly 4. Delivery and Presentation Delivery Method – Push : Reaches a consumer who is not currently interacting with the system – Pull : Notify consumers that personalized information is available but display this information only when the consumer explicitly requests it – Passive : displays personalized information as a by-product of other activities of the consumer Presentation – Ordered by relevance, unordered list of alternatives, or various types of visualization Center for E-Business Technology

Copyright  2008 by CEBT Personalization Process – (4) 5. Measuring Personalization Impact Various accuracy metrics can be used to evaluate the personalization – Consumer lifetime value, loyalty value, purchasing and consumption experience The quality of recommendations depends on the previous stages 6. Adjusting Personalization Strategy Feedback can be used to identify a part that needs improvements The quality of interaction should grow over time One of the main challenges of personalization is the ability to achieve the virtuous cycle of personalization and not to fall into the de-personalization trap Center for E-Business Technology

Copyright  2008 by CEBT Building Consumer Profiles  Traditionally, consumer profiles consist of simple factual information Name, gender, date of birth,.. The largest purchase value made at a Web site  Advanced behavioral information can be expressed by Conjunctive Rules – John Doe prefers to see action movies on weekends – Name = “John Doe” & Movietype = “action” -> TimeOfWeek=“weekend” Sequences – XYZ: StartPage -> Home&Gardening -> Gardening -> Exit Signatures – The data structure that are used to capture the evolving behavior learned from large data streams of simple transactions – Top 5 most frequently browsed product categories over the last 30 days – typedef struct { int product_id[5]; } profile; Center for E-Business Technology

Copyright  2008 by CEBT Matchmaking Technologies – (1)  Classification based on the recommendation approach Content-based Recommendations – Analyze the commonalities among the items the consumer has rated highly in the past. Then, only the items that have high similarity with the consumer’s past preferences would get recommended Collaborative Recommendations – Find the closest peers for each consumer, i.e., the ones with the most similar tastes and preferences. Then, only the items that are most liked by the peers would get recommended Hybrid Approaches – Combine collaborative and content-based methods – This combination can be done in many different ways Center for E-Business Technology

Copyright  2008 by CEBT Matchmaking Technologies – (2)  Classification based on the algorithmic technique Heuristic-based Techniques – Calculate recommendations based on the previous transactions made by the consumers – Find the person whose taste in movies is the closest to mine, and recommend me everything this person liked that I haven’t seen yet Model-based Techniques – Use the previous transactions to learn a model – Based on the movies that I have seen, a probabilistic model is built to estimate the probability of how I would like each of the unseen movies Center for E-Business Technology

Copyright  2008 by CEBT Matchmaking Technologies – (3)  Classification into simple and advanced  In terms of recommendation accuracy Hybrid >> Pure Content-based, Collaborative Approach Model-based >> Heuristic-based Approach Center for E-Business Technology Matchmaking Simple Content-based Collaborative Heuristic-based Advanced Hybrid Model-based

Copyright  2008 by CEBT Characterizing Personalization Tech.  Combining the profiling and the matchmaking classification,  There has been very little prior work done for the lower right quadrant of Table 1 Because most of the research has focused on single aspect There’s an important research opportunity in personalization technologies Center for E-Business Technology

Copyright  2008 by CEBT Future Work on Personalization Process  The integration of advanced profiling and matchmaking techniques  Other Issues Degree of personalization, privacy, scalability, trustworthiness, intrusiveness, and usage of various metrics to measure effectiveness of personalization  In the context of the process-oriented view of personalization Understanding the dynamics between various stages – Understand how much each stage contributes We need sophisticated evaluation metrics, and methods Feedback should be integrated carefully  The process-oriented view suggests the importance and the need for vertical personalization research Center for E-Business Technology

Copyright  2008 by CEBT Summary  Process Cycle  Research Opportunities Center for E-Business Technology

Copyright  2008 by CEBT Paper Evaluation  Good paper for beginners Tried to cover all aspects of personalization  Problems are suggested but no solution Center for E-Business Technology

Copyright  2008 by CEBT Discussion  How can evaluate the impact of each process Which measure will be worked – Mean Absolute Error – Customer Lifetime Value  Business vs. Research Center for E-Business Technology