Transcript Updated Presentation slides

Customer Personalization
Gio Wiederhold
Presented as Part of EPFL DL talk
Spring 2000
Customer `Joe's participation in interest groups
Volunteer
firefightes
Civil
War buffs
Joe
Skate
boarders
Software
managers
Software
manager
Inrests
of
swing
mgmen
Interests
of
software
managers
Volunteer
firefighter
Interests
of
volunteer
firefighters
Civil war
buff
Interests
of
Civil war
buffs
Skate
boarder
Interests
of
Skate
boarders
Intests
of
heave
moren
Interest models for types of customers
Customer is defined to be {a person
• arranging a vacation trip
 one specific interest/ task}
• 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
Customer:
• wants choices
Prolog
• Correctness
• The first answer
Optimization
• The best choice
• Assumes all
factors are known,
no human decision
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:
Speedy
Mike:
Greedy
Pete:
P1= P2, P3
P2, P1=P3
P1=P3, P2
Personal vs. Customer Model
Actual Person has multiple roles
1.
how to switch
a. explicitly - awkward
b. 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