Digital Trails Dave King 1 5 10 Part 2 D3
•Télécharger en tant que PPT, PDF•
1 j'aime•800 vues
![Collective Intelligence (CI): Defined ,[object Object],[object Object],Source: Alag, S. Collective Intelligence in Action . Manning Press (2009)](https://image.slidesharecdn.com/digitaltrails-daveking-1-5-10-part2-d3-100105094452-phpapp01/85/digital-trails-dave-king-1-5-10-part-2-d3-1-320.jpg)
Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (18)
En vedette
En vedette (20)
Similaire à Digital Trails Dave King 1 5 10 Part 2 D3
Similaire à Digital Trails Dave King 1 5 10 Part 2 D3 (20)
Plus de Dave King
Plus de Dave King (12)
Dernier
Dernier (20)
Digital Trails Dave King 1 5 10 Part 2 D3
- 2. Collective Intelligence: Explicit Resources
- 3. Ways to Harness CI Source: Alag, S. Collective Intelligence in Action . Manning Press (2009)
- 6. CI Data Model Most applications generally consist of users and items. An item is any entity of interest in your application. If your application is a social-networking application, or you’re looking to connect one user with another, then a user is also a type of item. Source: Alag, S. Collective Intelligence in Action . Manning Press (2009) Users Metadata Items
- 7. Classification of Recommender Engines
- 14. Demographic Recommendation: Mystery Movie
- 26. Collaborative Filtering: Cosine Similarity (an Example) Step 1: Find SQRT of Sum of Squares Each Row of Scores Step 2: Divide each Scores In row by SQRT of Sum of SQs Step3: Calculate Cosine Similarity Between Users by Summing X-Products of their normalized Scores (from Step 2)
- 27. Collaborative Filtering: User-Based Predictions and Recommendations
- 29. Collaborative Filtering: Item-Based Example www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf Amazon.com has more than 29 million customers and several million catalog items. Other major retailers have comparably large data sources. While all this data offers opportunity, it’s also a curse, breaking the backs of algorithms designed for data sets three orders of magnitude smaller. Almost all existing algorithms were evaluated over small data sets.
- 31. Collaborative Filtering: Item-Based Example
- 33. Collaborative Filtering: There's Money in CF – The Netflix Prize
- 34. Collaborative Filtering: Netflix Prize
- 36. Collaborative Filtering: Other Applications Anything that can be represented in matrix form where n is a number representing a nominal (e.g. 0,1 for present, absent), ordinal, interval or ratio value
- 38. CI from Content: Resources
- 41. CI from Content: Text Mining Process
- 43. CI for Unstructured Contents: Analyzing Blogs
- 44. CI for Unstructured Contents: Analyzing Blogs (RSS Feed)
- 45. CI for Unstructured Contents: Analyzing Blogs (Source of RSS Feed)
- 46. CI for Unstructured Contents: Structure of an “Atom” Feed
- 48. CI for Unstructured Contents: Word Counts for Collection of Blogs
- 50. CI for Unstructured Contents: Blog Dendogram
- 51. CI for Unstructured Contents: Blog Results for K-Means Clustering
- 53. CI from Content: Simple Example “We Feel Fine” Visualizations Madness Murmerings Montage Mounds Metrics Mobs
- 54. CI from Content: 9/11 Pager Data 2001-09-11 08:52:46 Skytel [002386438] B ALPHA Netdesk@nbc.com||Reports of a plane crash near World Trade Center - no more details at this point. WNBC's LIVE pix - Network working on coverage.
- 55. CI from Content: 9/11 Pager Data
- 58. Dataveillance Data Mining & Social Network Analysis ChoicePoint (17B records) Acxiom Equifax (400M credit holders) Experian … Internet & Other Communication Data Sources
Notes de l'éditeur
- Customers Who Bought On the information page for every item, Amazon shows the “Customers Who Bought” feature that recommends items frequently purchased by customers who purchased the selected item. The feature is also used on the shopping cart page. This works as the equivalent to the impulse items in a supermarket checkout line, but here the impulse items are personalized for each customer.
- Contest begins October 2, 2006 and continues through at least October 2, 2011 . Contest is open to anyone, anywhere (except certain countries listed below). You have to register to enter. Once you register and agree to these Rules, you’ll have access to the Contest training data and qualifying test sets. To qualify for the $1,000,000 Grand Prize, the accuracy of your submitted predictions on the qualifying set must be at least 10% better than the accuracy Cinematch can achieve on the same training data set at the start of the Contest. To qualify for a year’s $50,000 Progress Prize the accuracy of any of your submitted predictions that year must be less than or equal to the accuracy value established by the judges the preceding year. To win and take home either prize, your qualifying submissions must have the largest accuracy improvement verified by the Contest judges, you must share your method with (and non-exclusively license it to) Netflix, and you must describe to the world how you did it and why it works.