1 / 16

Chapter 8 Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering

Chapter 8 Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering. Data Mining Techniques So Far…. Chapter 5 – Statistics Chapter 6 – Decision Trees Chapter 7 – Neural Networks. Nearest Neighbor Approaches. Based on the concept of similarity

libitha
Download Presentation

Chapter 8 Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering

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. Chapter 8Nearest Neighbor Approaches:Memory-Based Reasoning and Collaborative Filtering

  2. Data Mining Techniques So Far… • Chapter 5 – Statistics • Chapter 6 – Decision Trees • Chapter 7 – Neural Networks

  3. Nearest Neighbor Approaches • Based on the concept of similarity • Memory-Based Reasoning (MBR) – results are based on analogous situations in the past • Collaborative Filtering – results use preferences in addition to analogous situations from the past

  4. Memory-Based Reasoning (MBR) • Our ability to reason from experience depends on our ability to recognize appropriate examples from the past… • Traffic patterns/routes • Movies • Food • We identify similar example(s) and apply what we know/learned to current situation • These similar examples in MBR are referred to as neighbors

  5. MBR Applications • Fraud detection • Customer response prediction • Medical treatments • Classifying responses – MBR can process free-text responses and assign codes

  6. MBR Strengths • Ability to use data “as is” – utilizes both a distance function and a combination function between data records to help determine how “neighborly” they are • Ability to adapt – adding new data makes it possible for MBR to learn new things • Good results without lengthy training

  7. MBR Example – Rents in Tuxedo, NY • Classify nearest neighbors based on descriptive variables – population & median home prices (not geography in this example) • Range midpoint in 2 neighbors is $1,000 & $1,250 so Tuxedo rent should be $1,125; 2nd method yields rent of $977 • Actual midpoint rent in Tuxedo turns out to be $1,250 (one method) and $907 in another.

  8. MBR Challenges • Choosing appropriate historical data for use in training • Choosing the most efficient way to represent the training data • Choosing the distance function, combination function, and the number of neighbors

  9. Memory-Based Reasoning Exercise • Work in teams of 3 or 4 • Time Limit = 10 minutes • Discuss a couple of ways in which MBR could be utilized and hence useful to an organization (enterprise, govt agency, etc.) • Teams present ideas

  10. Collaborative Filtering • Lots of human examples of this: • Best teachers • Best courses • Best restaurants (ambiance, service, food, price) • Recommend a dentist, mechanic, PC repair, blank CDs/DVDs, wines, B&Bs, etc… • CF is a variant of MBR particularly well suited to personalized recommendations

  11. Collaborative Filtering • Starts with a history of people’s personal preferences • Uses a distance function – people who like the same things are “close” • Uses “votes” which are weighted by distances, so close neighbor votes count more • Basically, judgments of a peer group are important

  12. Collaborative Filtering • Knowing that lots of people liked something is not sufficient… • Who liked it is also important • Friend whose past recommendations were good (or bad) • High profile person seems to influence • Collaborative Filtering automates this word-of-mouth everyday activity

  13. Preparing Recommendations for Collaborative Filtering • Building customer profile – ask new customer to rate selection of things • Comparing this new profile to other customers using some measure of similarity • Using some combination of the ratings from similar customers to predict what the new customer would select for items he/she has NOT yet rated

  14. Collaborative Filtering Example • What rating would Nathaniel give to Planet of the Apes? • Simon, distance 2, rated it -1 • Amelia, distance 4, rated it -4 • Using weighted average inverse to distance, it is predicted that he would rate it a -2 • =(0.5*-1 + 0.25*-4) / (0.5 + 0.25) • Nathaniel can certainly enter his rating after seeing the movie which could be close or far from the prediction

  15. Collaborative Filtering Exercise • Work in teams of 3 or 4 • Time Limit = 10 minutes • Discuss a couple of ways in which Collaborative Filtering could be utilized and hence useful to an organization (enterprise, govt agency, etc.) • Teams present ideas

  16. End of Chapter 8

More Related