Notes from “Recommendations as a Conversation with the User” by Daniel Tunkelang

Daniel Tunkelang

Daniel Tunkelang

These are my unedited notes from Daniel Tunkelang’s presentation at #recsys2011. I am editing as you are reading this post.

“Recommendations as a Conversation with the User” by Daniel Tunkelang
Goal is to have a better relationship with the user

Three take aways from this talk:

  • Consider asking vs guessing
  • Ask good questions
  • It’s okay to make mistakes if you have a good explanation and adapt to feedback

Theory

The Man Who Lied to His Laptop”  is a great related read
Paul Grice’s maxims of conversations:

  1. Quality
  2. Quantity
  3. Relation
  4. Manner

**Do not lie

  • Don’t use “recommended” when you really mean “sponsored” or “excess inventory”. User’s loss of trust will cost you. but users do not have a model on how on how to trust a system
  • Optimize for the user’s utility
  • Apply a standard of evidence (quality, quantity) that you believe in

Right amount of information

  • Exchange small units of information
  • If recommendations supplement other content consider overall cognitive load
  • provide short meaningful explanations

Maxim 3: Relation. Relevant to the user

  • Offer value to the user
  • respect task context
  • don’t be obnoxious

Maxim 4: Manner

  • relevant to the user
  • Eschew obfuscation
  • Avoid ambiguity
  • be brief
  • be orderly

Another perspective

Another perspective is Gary Marchionini’s perspective on Human computer information retrieval

Empower people to explore large-scale information but demand that people also take responsibly for the control be expending cognitive and physical energy

principles of hcir

  1. do more than deliver information: facilitate sense-making
  2. require and reward effort
  3. adapt to increasingly knowledgeable users over time
  4. be engaging and fun to use

Adapt to user knowledge
Systems that don’t get better over time will frustrate users, because users DO get better over time

Personalized recommendations

  • be transparent about model so users gain insight
  • allow users to modify models to correct
  • solicit just enough information to provide value
  • Exemplars are interesting tools to communicate the recommender model to the user
  • Users should be able to modify the recommender system say you have a recommender system that uses location and user is using a proxy. He should be able to turn if off to make it noncreepy!

Social recommnedations

  • identify the right set of similar users
  • allow users to manipulate the social lens
  • accommodate users who break your model

When making item recs, explain your recommendations! Watch for non-sequiturs (diapers -> beer problem)

**Tell me about yourself is friendlier than “fill out 20 pages of survey”

Corpse bride is in the recommnded set and I have watched it, it is good. it gives me the feeling that recommender is working properly

Learning from netflix

  • Ask users for help upfront but not too much help
  • pay attention to what the user tells you
  • give users value often and early

75% of netflix views result from recommendation

Underpromissing and overdelivering is sometimes a good idea

Soe models more explainabel than others

  1. consider decision trees and rule based models
  2. avoid using latent, unlabled features
  3. if the model is opaque use exaples as surrougates

Make a good first impression
your user’s first experience is critical

See “Machine learning for large scale recommender systems” by Agrawal and Chen ICL 2011 Tutorial

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~ by marksalen on October 24, 2011.

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