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

Personalization Technologies: A Process-Oriented Personalization Technologies: A Process-Oriented PerspectivePerspective

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

OutlineOutline

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


DefinitionsDefinitions

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 [www.personalization.com]

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



Personalization EnginePersonalization Engine

Personalized offers can be delivered from providers to consumers by personalization engines in three ways



Personalization Process - (1)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


Understand the visitors

Deliver personalized

content

Measure the personalization

impact


Personalization Process – (2)Personalization Process – (2)

The technical implementation of the Understand-Deliver-Measure cycle consists of the six stages


key issues



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




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



Building Consumer ProfilesBuilding 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;



Matchmaking Technologies – (1)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




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




Classification into simple and advanced

In terms of recommendation accuracy

Hybrid >> Pure Content-based, Collaborative Approach

Model-based >> Heuristic-based Approach


MatchmakingSimple

Content-basedCollaborative

Heuristic-based

AdvancedHybrid

Model-based


Characterizing Personalization Tech.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



Future Work on Personalization Future Work on Personalization ProcessProcess

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



SummarySummary

Process Cycle

Research Opportunities


Understand the visitors

Deliver personalized

content

Measure the personalization

impact


Paper EvaluationPaper Evaluation

Good paper for beginners

Tried to cover all aspects of personalization

Problems are suggested

but no solution



DiscussionDiscussion

How can evaluate the impact of each process

Which measure will be worked

– Mean Absolute Error

– Customer Lifetime Value

Business vs. Research


Documents

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