Recommender Systems. >1,000,000,000 Finding Trusted Information How many cows in Texas?
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Transcript of Recommender Systems. >1,000,000,000 Finding Trusted Information How many cows in Texas?
Recommender Systems
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>1,000,000,000
Finding Trusted Information
How many cows in Texas?
http://www.cowabduction.com/
Outline
What are Recommender Systems? How do they work? How can we integrate social information / trust?
What are some applications?
Netflix
Amazon
How do they work?
Two main methods Find things people similar to me like Find things similar to the things I like
Example with People
Who is a better predictor for Alice? Compute the correlation:
Bob 0.26 Chuck: 0.83
Recommend a rating of “Vertigo” for Alice Bob rates it a 3 Chuck rates it a 5 (0.26 * 3 + 0.82 * 5) / (0.26+ 0.82) = 4.5
Star Wars Jaws Wizard of Oz
The Godfather
2001
Alice 5 4 3 3 1Bob 3 5 2 5 1Chuck 4 3 2 2 2
Item similarity
Methods are more complex Computed using features of items E.g. genre, year, director, actors, etc.
Some sites use a very nuanced set of features
How good is a Recommender System?
Generally: error Error = My rating - Recommended Rating Do this for all items and take the average
Need alternative ways of evaluating systems Serendipity over accuracy Diversity
Trust in Recommender Systems
If we have a social network, can we use it to build trusted recommender systems?
Where does the trust come from? How can we compute trust? Some example applications
1. Your Favorite Movie 2. Your Least Favorite Movie
1. Some Mediocre Movie 2. Some Mediocre Movie 3. Some Mediocre Movie 4. Some Mediocre Movie 5. Some Mediocre Movie 6. Some Mediocre Movie 7. Some Mediocre Movie 8. Some Mediocre Movie
In-Class Exercise
Sample Profile
Movie Your RatingUser 7's RatingDifference
Jaws 10 4 6A Clockwork Orange 1 7 6North by Northwest 10 4 6Peeping Tom 1 7 6The Godfather 7 6 1The Matrix 2 4 5 1Elf 8 7 1Gone with the Wind 7 8 1Madagascar 4 3 11408 3 4 1
Knowing this information, how much do you trust User 7 about movies?
Factors Impacting Trust
Overall Similarity Similarity on items with extreme ratings
Single largest difference Subject’s propensity to trust
Propensity to Trust
0
20
40
60
80
100
120
140
160
1 2 3 4 5 6 7 8 9 10
Trust Value
Number of Ratings
Advogato
Peer certification of users Master: principal author or hard-working co-author of an "important" free software project
Journeyer: people who make free software happen
Apprentice: someone who has contributed in some way to a free software project, but is still striving to acquire the skills and standing in the community to make more significant contributions
Advogato trust metric applied to determine certification
Advogato Website
http://www.advogato.org/ Certifications are used to control permissions
Only certified users have permission to comment
Combination of certifications and interest ratings of users’ blogs are used to filter posts
MoleSkiing
http://www.moleskiing.it/ (note: in Italian)
Ski mountaineers provide information about their trips
Users express their level of trust in other users
The system shows only trusted information to every user
Uses MoleTrust algortihm
FilmTrust
Movie Recommender Website has a social network where users rate how much they trust their friends about movies
Movie recommendations are made using trust Recommended Rating = Weighted average of all ratings, where weight is the trust (direct or inferred) that the user has for the rater
Challenges
Can these kinds of approaches create problems?
Recommender Systems - recommending items that are too similar
Trust-based recommendations - preventing the user from seeing other perspectives
Conclusions
Recommender systems create personalized suggestions to users
Social trust is another way of personalizing content recommendations
Connect social relationships with online content to highlight the most trustworthy information
Still many challenges to doing this well