Testing a Recommender System for Self-Actualization

Daricia Wilkinson, Saadhika Sivakumar , Pratitee Sinha, Bart P. Knijnenburg

Clemson University

Authors

Daricia Wilkinson

Saadhika Sivakumar

Pratitee Sinha

Bart P. Knijnenburg

Example

VS

GoalsRecommender Systems for Self-Actualization aims to:

Support rather than replace decision making

Focus on exploration rather than consumption

Attempt to cover users’ various tastes

Research Plan

DEVELOP TEST EVALUATE

Research Plan

DEVELOP TEST EVALUATE

Algorithmic Features

Incorrect negative predictions

items = max(average predicted rating – user predicted rating)

“Things we think you’ll hate”

Low valued predictions are never recommended

allows users to correct or confirm low-valued predictions

Algorithmic Features

Unknown Preferences

items = max(predicted ratingmf – predicted ratingknn)2

“Things we have no clue about”

difficult for recommenders to predict items for which there is insufficient information about whether the user will like them or not.

we estimate the system’s confidence by computing the difference in user-predicted ratings for different algorithms

Algorithmic Features

Novel Items

users = max(% top rated items with (#ratings<threshold))items = min(#ratings)

“Things you’ll be among the first to try”

Addresses the cold start problem

users may at times actually be excited to try new things, even if it does not always fit their preferences.

Algorithmic Features

Controversial Items

items = max(var(neighbors’ predicted ratings))

“Things that are controversial”

“safe” recommendations do not challenge a user’s tastes beyond what is generally agreed upon as “good” among like-minded users

items that some like-minded users really like, but others hate

Research Plan

DEVELOP TEST EVALUATE

Testing

More things you might like ~300Things you might hate

Things we are not sure about

Things you’ll be among the first to try

Things that are controversial

In addition to the top 10 things you might like:

Testing

15 20 1

RATE RATE CHOOSE

Research Plan

DEVELOP TEST EVALUATE

Evaluation

http://bit.ly/umuai http://bit.ly/userexperiments

EvaluationAspects

Perceived Recommendation quality, diversity, novelty

System & Choice Satisfaction

Choice and tradeoff difficulty

Perceived taste coverage

Fear of missing things

Taste clarification potential

Taste development potential

Perceived choice conformity

Behavior

Objective coverage

Objective choice conformity

Existing scales

New scales

Summary

New direction for recommenders that supports our aspirational selves

Contribution to the theory of recommender systems evaluation

Acknowledge multidimensionality of users

Thank You

Daricia Wilkinson

Saadhika Sivakumar

Pratitee Sinha

Bart P. Knijnenburg

dariciw@clemson.edu bartk@clemson.edussivaku@g.clemson.edu psinha@g.clemson.edu