1 / 17

Gift Channel

Gift Channel. Aaron Sun, Won Cho, Hong Huo, Ping Yan. Agenda. Value Proposition and Business M odel Market analysis System Diagram System Infrastructure System C omponents Gift idea recommendation with data mining Novelty. Value Proposition and Business Model. Value proposition

howard-stanley
Download Presentation

Gift Channel

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Gift Channel Aaron Sun, Won Cho, Hong Huo, Ping Yan

  2. Agenda • Value Proposition and Business Model • Market analysis • System Diagram • System Infrastructure • System Components • Gift idea recommendation with data mining • Novelty

  3. Value Propositionand Business Model • Value proposition • Direct your friends to your wishlist • Find gifts that your friends would like • Create your own wishlists based onGift Channel’s recommendations • Vision of Gift Channel • A community of various social networks • Business model • Commission from Amazon’s Associate Program • Referral fees from online retailers • Advertisements

  4. Market analysis • Amazon • not a competitor but a collaborator/supplier • Target customer • Online shoppers, women, young generations • Online gift market • Big potential (30% or more expected growth)* * Business Communications Review. Hinsdale: Jul 2005. Volume 35, Issue 7; p. 6

  5. Log-in Operational DB Recommendation algorithm Create Wishlist Wishlists Mining DB Find a Friend’s wishlist Wishlist Pattern Analysis Product Searching AMAZON API Share Wishlist Amazon user wishlists Amazon Production info System diagram

  6. System Infrastructure • J2EE: JSP, Java, Log4j • Servers: Tomcat, MySQL (Operational DB), MS-SQL (Mining DB) • IDE: Eclipse • Visualization Tool: JFree Chart • Graphics: Photoshop

  7. Manage a wishlist A P I Collecting wishlists Amazon Wishlist info Data mining Recommendation Wishlist data store Recommended Products add Search by name • Your wishlist • Item #1 delete • item #2 delete Retrieved wishlist

  8. Product searching A P I Input search Messaging Business logic Amazon products info Java beans Request Display Parse XML Servlet JSP

  9. Operational DB User accountand wishlist information Wishlists Mining DB 6,428 users’ wishlists and79,556 records retrieved (using random last name selection) Database Design User Wishlists Wishlist items User frequency Item frequency Recommendation Wishlists Wishlist items Friends Amazon API

  10. Data mining for gift idea User-Item Matrix Representation of Input Data Item frequency Item frequency Item frequency Wishlists Mining DB • Remarks: sample 9,927records from the original dataset Reference: B.M. Sarwarm, et al., “Analysis of Recommendation Algorithms for E-Commerce”, ACM Conf. Electronic Commerce, ACM press, 2000, pp.158-167.

  11. Recommendation Algorithm • User-based collaborative filtering algorithm • User-based cosine vector similarity • Clustering: Prim’s Algorithm • Recommend items based on frequency ranking of how many similar customers purchase it

  12. Shopping preferencesvisualization JFreeChart is a free Java class library for generating charts

  13. Price visualization JFreeChart is a free Java class library for generating charts

  14. What is Novel about our website? • Personalized recommendation based on similar customers using clustering algorithm • Friend networking • Visualization • Shopping preference • Price range • Expandability to other online retailers (e.g. eBay)

  15. Datasets for Datamining • Total population- The whole dataset retrieved from Amazon- 6,428 users- 58,217 items (in terms of variety)- 79,556 records • Sample- The dataset used for the clustering- Criterion: item frequency > 4 and user value index > 5- 2064 users- 1,303 items (in terms of variety)- 9,927 records

  16. The Total Population Analysis ~

More Related