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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
Walkthrough Experiment Results ConclusionDesign
Prior Work
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
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
Walkthrough Experiment Results ConclusionDesign
Challenges
Walkthrough Experiment Results ConclusionDesign
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
Walkthrough Experiment Results ConclusionDesign
Interface Algorithm
Walkthrough Experiment Results ConclusionDesignDesign
Walkthrough
Interface
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Algorithm
Walkthrough Experiment Results ConclusionDesign
Latent Taste Space
Perform a non-negative matrix factorization
of ratings data
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
Recap
Some differences between rounds
1, 2, and 3+
Walkthrough Experiment Results ConclusionDesign
Round 1
Choose diversemovies from alarge region
Walkthrough Experiment Results ConclusionDesign
Round 2
Choose diversemovies from amedium region
Walkthrough Experiment Results ConclusionDesign
Round 3+
Choose diversemovies from asmall region
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
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?
Walkthrough Experiment Results ConclusionDesign
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
Walkthrough Experiment Results ConclusionDesign
Experimental Conditions
Walkthrough Experiment Results ConclusionDesign
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
Experimental Conditions
Walkthrough Experiment Results ConclusionDesign
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.
Walkthrough Experiment Results ConclusionDesign
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.
Walkthrough Experiment Results ConclusionDesign
Data Collection
Walkthrough Experiment Results ConclusionDesign
RQ1 RQ2
Results
RQ1 Is this tool useful? For what tasks? How does itcompare to traditional top-N recommenders?
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
MovieExplorer Tool MovieLens Interface
Walkthrough Experiment Results ConclusionDesign
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.
Walkthrough Experiment Results ConclusionDesign
RQ2 Is the navigational algorithm important?
Walkthrough Experiment Results ConclusionDesign
Average user satisfaction
Walkthrough Experiment Results ConclusionDesign
Average number of movies each user…
Walkthrough Experiment Results ConclusionDesign
RQ2 Is the navigational algorithm important?
Yes, and users prefer the real algorithm.
Walkthrough Experiment Results ConclusionDesign
Walkthrough Experiment Results ConclusionDesign
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
Design Experiment Results ConclusionOverview Walkthrough Experiment Results ConclusionDesign
Design Experiment Results ConclusionOverview Walkthrough Experiment Results ConclusionDesign
Questions?