1. Personalization in e-Commerce Dr. Alexandra Cristea a.i.cristea@warwick.ac.uk http://www.dcs.warwick.ac.uk/~acristea/

2. Introduction Benefits Perspectives Ubiquitous Computing 1. Contents

3. Introduction E-commerce: –The conducting of business communication and transactions over networks and through computers. –the buying and selling of goods and services, and the transfer of funds, through digital communications Others: all inter-company and intra-company functions (such as marketing, finance, manufacturing, selling, and negotiation) –B2B: business interactions between enterprises –B2C: interactions between enterprise and customers

4. Benefits First: “Hello Johnny!” syndrome Cost as issue 2005 onwards: Customer-Centric services for CRM (customer-relationship-management), –which can flexibly react to dynamically changing market requirements Customer Data Integration (CDI) services

5. Amazon

7. Perspectives: Use of adaptation Often simple business rules, allowing e.g., administrators to offer discounts on the basis of products selected by customers

8. Perspectives: Personalized Features (e.g., BroadView: www.broadvision.com)www.broadvision.com Push: system is pro-active Pull: system relies on the user who requests information Also: –qualifier matching, –simple rule-based matching : business rules E.g., generation of electronic coupons (based on previous purchases) that are sent by e-mail to each customer who has not purchased goods for a while

9. Perspectives: Personalized Product Recommendations Generalized –Interactive, dynamic taxonomies –Customer behaviour (customers who bought) –Item similarity (or correlation) Personalized –Content-based (e.g. content-based filtering: past and present of user) versus social recommendations (collaborative filtering) – pros & cons; –hybrid recommender systems –Item-to-item collaborative filtering (similarity to content based; item similarity, but lightweight, without user – for stable products)

10. Perspectives: Customer info sharing As a solution to latency (cold start): central UM Issues?

11. Perspectives: Personalized Product Info … leading to a sale –E.g., evaluation-oriented (as a car-sales person)

12. Case Study: SeTA sorting items on a suitability basis, to the preferences of their beneficiary. Individual UM (direct: questionnaires + monitoring) & indirect (stereotype) demographic data (e.g., age, job), & preferences for products (e.g., products). Prologue and summary tailored to user User + vendor interests represented Comparison table is allowed

13. Beginner (non expert)

14. Advanced (expert user)

15. Conclusions Case Study SeTA Positive: advanced UM, dynamic content generation techniques, personalized recommendation: generation of electronic catalogs meeting individual user needs with high accuracy. Negative: knowledge intensive approach supporting the system adaptation which may discourage web designer.

16. Perspectives: CRM customer-centered instead of product- centered share of customer, replacing traditional share of market. accurate UM can then support the proposal of personalized offers to improve the customer’s loyalty and thus the company’s profit, in the medium-long term mass customization Cross-selling, up-selling

17. Perspectives: Mass Customization Custom-design (for real!) Issues: costly (for firm) ; difficult (for customer) Adaptation can help with the latter via intelligent interaction with the buyer

18. Context-aware and Ubiquitous Computing in e-Commerce accessing a service anytime, anywhere and via different types of (mobile) devices. M-Commerce: commercial transactions performed by using wireless devices –E.g., digital wallets, push information services, and location-based services (e.g., visiting a museum, or attending a concert, or driving on a motorway) –Issues: power, bandwidth, efficiency, screen size limitations

19. Ubiquitous m-Commerce Perspectives generation of product and service presentations whose length is tailored to the screen size. layout of the user interface to the characteristics of the device used to access the service. (via HTML or XML processing, e.g.)

20. Conclusions & Discussion Here: B2C Potential personalization also in B2B –Quality of Service (QoS) levels (web) Service discovery, composition, execution –Web Services description languages, e.g. WSDL enable the specification of service public interfaces. –Web Service orchestration languages, e.g., WS-BPEL, support the definition of composite services based on the orchestration of multiple providers within possibly complex workflows –Semantic Web techniques have been used to add personalization to Web Services

21. Conclusions Personalization in e-Business: yes, if: –Supporting CRM (cust-rel-mng) –Enhancing usability –Enhancing interoperability

22. Any questions?