Building an Interactive Exploration Tool from Ratings and ...Design Walkthrough Experiment Results...

MovieExplorer Building an Interactive Exploration Toolfrom Ratings and Latent Taste Spaces

Taavi T. Taĳala Martijn C. Willemsen Joseph A. Konstan

Outline

Goals Prior Work Challenges

Design

Walkthrough Experiment Results ConclusionDesign

Interface Algorithm

Walkthrough

Walkthrough Experiment Results ConclusionDesignDesign

Research Questions Experimental Conditions

Participation Data Collection

Experiment

Walkthrough Experiment Results ConclusionDesign Walkthrough

RQ1 RQ2

Results

Walkthrough Experiment Results ConclusionDesign Experiment

Our Contributions Questions?

Conclusion

Walkthrough Experiment Results ConclusionDesign Results

Design Experiment Results ConclusionOverview Walkthrough Experiment Results ConclusionDesign

Goals Prior Work Challenges

Design

1. Support short-term needs and interests 2. Allow for exploration and novel item discovery

Goals


Prior Work



Critiquing-Based Recommenders: Survey

& Emerging TrendsChen and Pu (2012)

Requires itemattributes orcontent data

Conversational Collaborative

Recommendation: An Experimental AnalysisRafter and Smyth (2005)

Relies on user-usercollaborative filtering

One piece of feedback per interaction


Challenges


Items Shown What items do we show to support navigation?

1. Landmarks: recognizable, at user location 2. Navigation: recognizable, surround user location 3. Novel: not recognizable, at user location


Interface Algorithm

Walkthrough Experiment Results ConclusionDesignDesign

Walkthrough

Interface


Algorithm


Latent Taste Space

Perform a non-negative matrix factorization

of ratings data


Recap

Some differences between rounds

1, 2, and 3+


Round 1

Choose diversemovies from alarge region


Round 2

Choose diversemovies from amedium region


Round 3+

Choose diversemovies from asmall region



Research Questions Experimental Conditions

Participation Data Collection

Experiment

Research QuestionsRQ1: Is this tool useful? For what tasks? How does it

compare to traditional top-N recommenders? RQ2: Is the navigational algorithm important? RQ3: Does it matter what type of feedback we let a

user provide? RQ4: Does it matter what a user’s starting location is?


RQ1: Is this tool useful? For what tasks? How does it compare to traditional top-N recommenders?

RQ2: Is the navigational algorithm important? RQ3: Does it matter what type of feedback we let a

user provide? RQ4: Does it matter what a user’s starting location is?

Research Questions


Experimental Conditions


Algorithm 80% real 10% top-N

10% random

Feedback 50% positive-only 50% positive-negative

Starting Location

33% non- personalized

33% long-term personalized

33% short-term personalized

Participation Recruited 1,950 users over a 25-day period

using a banner ad on the MovieLens homepage. Observed each participant for a

35-day period following recruitment.


1. Surveyed users after first use, with questions on task usefulness, task preference, and user satisfaction.

2. Logged user interaction data, such as movies rated and/or wishlisted.


Data Collection


RQ1 RQ2

Results

RQ1 Is this tool useful? For what tasks? How does itcompare to traditional top-N recommenders?


MovieExplorer Tool MovieLens Interface


RQ1 Is this tool useful? For what tasks? How does itcompare to traditional top-N recommenders?

Useful for exploration and short-term needs tasks. Many prefer it over MovieLens for these tasks.


RQ2 Is the navigational algorithm important?


Average user satisfaction


Average number of movies each user…


RQ2 Is the navigational algorithm important?

Yes, and users prefer the real algorithm.



Our Contributions Questions?

Conclusion

1. Algorithm for navigating latent spaces 2. Interface that accepts multiple pieces of feedback

per interaction 3. Outline of the design space 4. Online field study that validated our tool’s

usefulness

Our Contributions



Questions?

Building an Interactive Exploration Tool from Ratings and ...Design Walkthrough Experiment Results...

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