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Matchin: Eliciting User Preferences with an Online Game

Matchin: Eliciting User Preferences with an Online Game. Severin Hacker, and Luis von Ahn Carnegie Mellon University SIGCHI 2009. Matchin. A game that asks two randomly chosen partners "which of these two images do you think your partner prefers?". Some Findings.

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Matchin: Eliciting User Preferences with an Online Game

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  1. Matchin: Eliciting User Preferences with an Online Game Severin Hacker, and Luis von Ahn Carnegie Mellon University SIGCHI 2009

  2. Matchin • A game that asks two randomly chosen partners "which of these two images do you think your partner prefers?"

  3. Some Findings • It is possible to extract a global "beauty" ranking within a large collection of images. • It is possible to extract the person's general image preferences. • Their model can determine a player's gender with high probability.

  4. A Taxonomy of Methods • Absolute Versus Relative Judgments • Total Versus Partial Judgments • Random Access Versus Predefined Access • "I Like" Versus "Others Like" • Direct Versus Indirect

  5. Existing Methods • Flickr Interestingness • Voting • Hot or Not

  6. The Mechanism • Matchin is a two-player game that is played over the Internet. • Every game takes two minutes. • One pair of images usually takes between two to five seconds. • Matchin uses a collection of 80,000 images from Flickr that were gathered October 2007.

  7. The Scoring Function • Matchin uses a sigmoid function for scoring games. • Constant scoring function • Players could get many points by quickly picking the images at random. • Exponential scoring function • The rewards sometimes became too high

  8. The Data • The game was launched on May 15, 2008. • Within only four months, 86,686 games had been played by 14,993 players. • There have been 3,562,856 individual decisions (clicks) on images. • An individual decision/record is stored in the form: • <id, game_id, player, better, worse, time, waiting_time>

  9. Ranking Functions • Empirical Winning Rate (EWR) • ELO Rating • TrueSkill Rating

  10. Empirical Winning Rate (EWR) • Function: • Two problems: • For images that have a low degree, the empirical winning rate might be artificially high or low. • It does not take the quality of the competing image into account.

  11. ELO Rating (1/2) • The ELO rating system was introduced for rating chess players. • Each chess player’s performance in a game is modeled as a normally distributed random variable. • The mean of that random variable should reflect the player’s true skill and is called the player’s ELO rating.

  12. ELO Rating (2/2) • Expected score: • ELO rating:

  13. Every player’s skill s is modeled as a normally distributed random variable centered around a mean μand per-player variance σ2. A player’s particular performance in a game then is drawn from a normal distribution with mean s and a per-game variance β 2. TrueSkill Rating (1/2)

  14. TrueSkill Rating (2/2) • Update: • Conservative skill estimate:

  15. Collaborative Filtering (1/2) • In the collaborative filtering setting, they want to find out about each individual's preferences • recommend images to each user based on his/her preferences • compare users and images with each other • They have developed a new collaborative filtering algorithm they call “Relative SVD”

  16. Collaborative Filtering (2/2) • The user feature vectors: • The image feature vectors: • The amount by which user i likes image j • Data: a set D of triplets (i,j,k) • The error for a particular decision: • The total sum of squared errors (SSE):

  17. Comparison of the Models

  18. Local Minimum • Do humans learn while playing the game? • They compared the agreement rate of first-time players and other players. • the first-time players: 69.0% • the more experienced players: 71.8% • They have also measured if people learn within a game. • the first half of the game: 67% • the second half of the game : 64%

  19. Gender Prediction • The conditional entropy: • The necessary conditional probabilities Pr(G=g|X=x) can be computed with Bayes' rule given the class conditionals Pr(X=x|G=g). • The naïve Bayes classifier will maximize the likelihood of the data: • The total accuracy is 78.3%

  20. The Top Ranked Images

  21. Discussion (1/2) • The highest ranked pictures • sunsets, animals, flowers, churches, bridges, and famous tourist attractions • neither provocative nor offensive • The worst pictures • taken indoors and include a person • blurry or too dark • screenshots or pictures of documents or text

  22. Discussion (2/2) • There are substantial differences among players in judging images, and taking those differences into account can greatly help in predicting the users’ behavior on new images. • More experienced players had about the same error rate as new players.

  23. Conclusion • The main contribution of this paper is to provide a new method to elicit user preferences . • They compared several algorithms for combining these relative judgments into a total ordering and found that they can correctly predict a user’s behavior in 70% of the cases. • They describe a new algorithm called Relative SVD to perform collaborative filtering on pair-wise relative judgments. • They present a gender test that asks users to make some relative judgments and can predict a random user’s gender in roughly 4 out of 5 cases.

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