1. Sunilkumar S. Manvi and P. Venkataram Protocol Engineering and Technology Unit, ECE Dept. Indian Institute of Science Bangalore, 560 012, INDIA e-mail: sunil@protocol.ece.iisc.ernet.in http://pet.ece.iisc.ernet.in/ “Presented by S.S.Manvi at ITPC 2003, Kathmandu, Nepal

2. Introduction to E-commerce Related work Objectives of proposed work E-commerce information-services model Simulation Conclusions Organization of Talk “Presented by S.S.Manvi at ITPC 2003, Kathmandu, Nepal

3. E-commerce is perceived as one of the killer applications of the computer and communication technologies. E-commerce can be defined as "the buying and selling of information, products, and services via Computer networks". It has changed the way the sellers distribute their products and services to the customers. Approaches to online shopping: Visiting a vendor site Recommender systems Comparison-shopping E-commerce effectiveness and revenues can be increased by providing efficient information services to customers subject to their behavior. E-Commerce “Presented by S.S.Manvi at ITPC 2003, Kathmandu, Nepal

4. Buyer mobile agents and seller agents (static) SAFER: It provides services for agents in e-commerce and establishes a rich set of mechanisms to manage and secure them. Recommender systems based on voluntary ratings, reviews of products Six stages which elucidate agent technology: Need Identification, Product Brokering, Merchant Brokering, Negotiation, Purchase and Delivery, Product Service and Evaluation. Comparison shopping agents: shopbots and pricebots, BANDELLO, MYSIMON, Surf and call. Intelligent shopper: learns user personal preferences and autonomously shops on their behalf while protecting their privacy. Related Work “Presented by S.S.Manvi at ITPC 2003, Kathmandu, Nepal

5. Help shoppers zero in on a product they want to buy and determine which vendor they want to buy it from based on price, reputation, product availability and service. Website should be user friendly and easy to navigate with proper information presentation. Sufficient information for high budget products to increase buyers confidence. Multimedia stream presentations should be used Since presentations require more network resources, identify the genuine buyers. Customer behavior based on user visits and purchases. There is a need to bind the relation between customer's behavior and he needs in order to cater the relevant information services to a customer. Needs of Information Services in E-Commerce “Presented by S.S.Manvi at ITPC 2003, Kathmandu, Nepal

6. To provide three types of information service (technical details, service conditions, demos, etc.) presentations based on the customer behavior. The types of presentations are: 1)Some random clips and detailed specific features of a product; 2)Random clips of a product; 3)Detailed presentations of a product. To use a distributed proxy based E-commerce information services model, which is intelligent enough to study the customer behavior and plan the presentations according to customer buying probability. Objectives of Proposed Work “Presented by S.S.Manvi at ITPC 2003, Kathmandu, Nepal

7. The purpose of using distributed stream servers: is to facilitate adaptation to failures, distributes the load across the network. Zone wise proxy servers for speed up of services Ecommerce Information Services model “Presented by S.S.Manvi at ITPC 2003, Kathmandu, Nepal

8. Virtual market place; Learns the customer behavior; Plans intelligent multimedia presentations Allows vendor to store data streamwise Presentation access based on resources, past history Types of multimedia presentations : Type 1 (random + detailed), Type 2 (random), Type 3 (detailed). Point synchronization for Type 1, real-time synchronization for Type 2, Adaptive synchronization for Type 3. Salient Features “Presented by S.S.Manvi at ITPC 2003, Kathmandu, Nepal

9. Intelligent mobile agent for comparison charts preparation Registration of users Updating history Allows browsing of products, multimedia presentations Intelligently caches the product information presentations Prevent direct connection between vendors and customers, and thereby hides identity of a customer from vendors. Optimal usage of resources based on customer behavior Computation of Product BP and RASR of a Customer Classification of customers under presentation category Scheduling the presentations based on RASR Functions of Proxy Shopping Server “Presented by S.S.Manvi at ITPC 2003, Kathmandu, Nepal

10. Begin 1. Choose weight (w1) for previous purchases and weight (w2) for buying interest such that w1+w2=1.0; 2. Compute product purchase probability (PP): PP=number of times purchased during a visit/ number of visits to site; 3. Compute buying interest probability (BIP): BIP=number of viewed pages of product/ number of pages of product; 4. Compute customer buying probability (BP) =PP*w1 + BIP*w2; 5. Compute the customer resource access success ratio (RASR): RASR=number of times successful in resource access/ number of times resources requested for presentation; 6. Stop End. Computation of Product BP & RASR of Customer “Presented by S.S.Manvi at ITPC 2003, Kathmandu, Nepal

11. Begin 1. If (customer BP > 0.75) then Classify customer under Type 3 presentation; 2. If (customer BP > 0.50 and customer BP<=0.75 ) then Classify customer under Type 1 presentation; 3. If (customer BP <= 0.50) then Classify customer under Type 2 presentation; 4. Stop. End. Customer Classification : Presentation Category “Presented by S.S.Manvi at ITPC 2003, Kathmandu, Nepal

12. Begin 1. Sort the presentation requesting users according to RASR in ascending order; 2. If (Requesting users <=max_cust_support) then Provide presentations to all the requesting users with respect to their classified presentation; Else, Select only the first max_cust_support customers for presentations from the sorted list, and present them with respect to their classified presentation; 3. Stop. End. Scheduling the presentation based on RASR “Presented by S.S.Manvi at ITPC 2003, Kathmandu, Nepal

13. The number of visits (mv) made by a customer to proxy server are randomly distributed between 1 and maxv, where, maxv is the maximum visits. The number of times the customer purchased (pr) among the visits is randomly distributed between 1 and mv. The number of pages viewed for a product by the customer is randomly distributed between 1 and mp, where mp is the maximum pages of a product. We assigned equal probability to customer buying and buying interest probability. Customer resource access ratio is randomly distributed between 0 and 1 Maximum number of customers supported by a proxy for presentations are max_cus_sup. Probability of a logged in user (registered users only) asking for product presentation is Bernoulli distributed (Pup). Simulation “Presented by S.S.Manvi at ITPC 2003, Kathmandu, Nepal

14. Number of customers in a presentation category: It is defined as the number of customers at a proxy classified under different presentation categories based on buying probability. Number of customers given resource access: It is defined as the number of customers successful in accessing the resources for presentation among the total number of customers requesting for presentation at a given instant of time. The input values considered for simulation are: maxv= 20; mp = 10; max_cus_sup = 100; w1=w2= 0.5; Pup is considered as 0.2, 0.5 and 1.0; number of customers at the proxy are varied from 100 to 500. Simulation “Presented by S.S.Manvi at ITPC 2003, Kathmandu, Nepal

15. Results “Presented by S.S.Manvi at ITPC 2003, Kathmandu, Nepal

16. Results “Presented by S.S.Manvi at ITPC 2003, Kathmandu, Nepal

17. Proposed a distributed proxy based E-shopping model Intelligent presentations based on customer behavior. The benefits of the scheme are: flexible product-information collection using agents, intelligent product-information presentation planning, speeding up the shopping services by using distributed proxies, flexible and adaptable presentation of product-information services. Dynamic assignment of weights could be related to customer buying pattern, Attitude can be judged based on the customer behavior in the past (either in weeks or months), resources utilized, amount of purchase. Conclusions “Presented by S.S.Manvi at ITPC 2003, Kathmandu, Nepal