Brokering. Overview to Brokering Agents and E-Commerce. Presentation Schedule. Question. Online Retail Sales. Outline
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1 Overview to Brokering Agents and E-Commerce Byung-Sun Yang Microarchitecture and System Software Lab. School of EE Brokering!Dark implication? Exploit customer and seller Linda Kim!Trade mediation Help customer and seller Much room for automization : agent More important in E-commerce Presentation Schedule!Overview to Brokering Agents and E- Commerce :!Kasbah :!Tete-a-Tete and MARI :!Open Agent-based Market Infrastructure : Question!Explain the six stages in the consumer buying behavior model. Outline!E-commerce and Brokering Agents!Consumer Buying Behavior Model!Examples of Brokering Agents!AI Techniques and E-commerce!Summary Online Retail Sales!1996 $600 Million!1997 > $2 Billion $800 Million between Thanksgiving and Christmas!2001 $17 Billion 1
2 New World of E-commerce Electronic Commerce Business opportunities:! efficient channel! large market Customer opportunities:! choice! convenience Business challenges:! customer attention! customer loyalty Customer challenges:! finding relevant stuff! trust&privacy! Internet Economics &Pricing! Marketing & Advertising! Payment Mechanisms! Security & Privacy! Trust & Reputation! Law & Contracts! Back-Office Management " Supply Chain Management " Product Ontologies " Multimedia Shopping Environments " Selling Goods & Services On-line "... Brokering Agents : Key of EC USER BUY AGENT SELL AGENT BIZ user purchasing needs, criteria & preferences business sale & pricing rules Consumer Buying Behavior!Need identification : stimulation!product brokering : what to buy!merchant brokering : from whom to buy!negotiation : choice and decision!purchase and delivery!product service and evaluation negotiation & transaction Examples of Brokering Agents PersonaL ogic Bargain Finder Jango Kasbah Auction Bot Tete-a- Tete Firefly Need Identification X Product Brokering X X X X Merchant Brokering X X X X Negotiation X X X Purchase and Delivery Evaluation x Need Identification!Consumer becomes aware of unmet need. New product information Recommendation!Amazon.com s Eyes program!firefly in Barnes and Noble Alerts customer with recommendation Based on collaborative filtering 2
3 Product Brokering PersonaLogic! Consumer determines what to buy according to personal criteria. Critical evaluation of retrieved product information Result : consideration set of products! PersonaLogic : AOL.com decision maker Constraint-based filtering! Firefly : Barnes and Noble Automated collaborative filtering : word of mouth! Tete-a-Tete PersonaLogic (CSP) PersonaLogic (Result) PersonaLogic (Comparison) Firefly 3
4 Merchant Brokering!Customer determines from whom to buy. Evaluation of merchant alternatives price, warranty, availability, delivery time Comparison shopping agents!bargainfinder : the first agent!jango : Excite, Inc.!Kasbah Merchant Blocking Issue!Current merchant brokering = on-line price comparison!some merchants do not want to compete on price alone. Accesses from brokering agents are blocked.!jango : aggressive interoperability Product requests originate from consumer s Web browser. BargainFinder Jango (Original) catalog format changes broke BF one third of the merchants blocked BF but other merchants wanted to be included average user bought a second CDs w/o BF Jango in Excite Negotiation!Price and other terms of transactions are determined. Stock market, auction house, ad-hoc haggling!merchants don t have to determine the price apriori. Fair resource allocation!more necessary for B2B than retail!auctionbot, Kasbah, Tete-a-Tete 4
5 AI Techniques and E-Commerce!Software agents deal with the cumbersome jobs such as collecting/analyzing information and transactions.!recommender systems!user-interface!negotiation mechanisms!infrastructure, languages, protocols Recommender Systems (1)!Content-based filtering Mainlyusedinmerchantbrokering Wrappers Hand-coded : BargainFinder Automated : Jango (50% success rate) API : AuctionBot!Collaborative-based filtering Feedback and rating Firefly and on-line stores Recommender Systems (2)!Constraint-based filtering Mainly used in product brokering PersonaLogic, Tete-a-Tete Considers relevance with features of items Formulation of problem and solution space variables, domains, constraints General CSP(constraint satisfaction problem) techniques are available. Backtracking and backjumping User-Interface! Adaptable and flexible user-interface Different shopping manners Currently, electronic catalog Enhanced price list Virtual shopping world 3D VRML shopping mall Personalized sales agent avatars! Trust in agent systems Predictable and explainable behavior CSP system > collaborative system. Simple and predictable agents > smart and adaptable agents Infrastructure, Languages, Protocols!Construct a full consumer buying system Mix several types of agents for CBB stages!xml : extensible Markup Language Data content meta language Appropriate for the integration Tags must be semantically consistent.!edi : Electronic Data Interchange ProtocolforB2B Summary!Consumer buying behavior model Agents automate CBB stages.!information filtering is necessary. Content-based Collaborative-based Constraint-based!IAusagewillexpandtoB2Bandsupply chain management. 5
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