Customer Personalization

Gio Wiederhold

Presented as Part of EPFL DL talk

Spring 2000

Software manager

Volunteer firefighter

Civil warbuff

Interests of

software managers

Skateboarder

Interests of

Skateboarders

Interests of

Civil war buffs

Interests of

volunteerfirefighters

Inrests of swing mgmen

Intests of

heave moren

Civil War buffsVolunteer

firefightes

Skateboarders

Joe

Customer `Joe's participation in interest groups

Software managers

Interest models for types of customers

Customer is defined to be {a person one specific interest/ task}

• arranging a vacation trip• activity/interests location town days hotel by grade flight / tour bus public transport rented car

• arranging a business trip• location & date hotel by corp. plan flight taxi, limo, or rented car

• getting a computer for Joe Cheap• search CPU by price modem display

• getting a computer for Peter Fast • search CPU by speed storage display network

• A customer interest model is Hierarchical computable, unambiguous

• alternatives at each level ( evaluate, closure, commit, rollback )

Example: Result modes for ranking

Databases:

• Completeness

• All the answers

Prolog

• Correctness

• The first answer

Optimization

• The best choice• Assumes all factors are known, no human decision

Customer:

• wants choices

also (but rarely invoked)

• explanation for trust

• provider background

Ranking

Qualitative Significant Differences:

in terms of the customer model

Plan 1. UA59 dep.Wash.Dulles 17:10, arr. LAX 19:49

Plan 2. AA75 dep.Wash.Dulles 18:00, arr. LAX 20:10

Plan 3. UA119 dep.Wash.Dulles 9:25, arr. LAX 12:00

Busy Joe:

P1= P2, P3

Speedy Mike:

P2, P1=P3

Greedy Pete:

P1=P3, P2

Personal vs. Customer Model

Actual Person has multiple roles1. how to switch

a. explicitly - awkwardb. implicitly - hard to perform fast

2. keep past contexts return to prior local state

Switching rate will differ• work versus fun• adequacy of models

Concept not yet proven • experimentally• in practice

Combining the models*

Identify articulations• Match customer and resource terms

• semantic mismatches• thesauri, matching rules

Match level of detail • Match customer and resource values, summarize numbers, result ranks

• completeness, unit mismatches, text• indicate constraints in models• textual abstraction • input for visualization