Recommender Systems

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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