Notes from “Recommendations as a Conversation with the User” by 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:
- Quality
- Quantity
- Relation
- 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
- do more than deliver information: facilitate sense-making
- require and reward effort
- adapt to increasingly knowledgeable users over time
- 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
- consider decision trees and rule based models
- avoid using latent, unlabled features
- 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
