Trust-aware Bootstrapping of Recommender Systems

Paolo Massa, Paolo Avesani

ITC/IRST, Trento, Italy

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 2.5 License.

.

The problem

The problem: bootstrapping of RS for new users

Outline

Collaborative Filtering Recommender Systems

Users explicitly assign ratings to items (ex: "I like Titanic as 4/5")

Collaborative Filtering (CF) RSs

 Task: generating recommendations for the active user ME

Collaborative Filtering RSs: example

Itm1 Itm2 Itm3 Itm4
ME 2 5 ? 5
User1 5 2 3
User2 4 1 3
User3 2 5 5 4

Step 1: Find users similar to ME (neighbours)

Step 2: Predict rating of user ME to Item3 as weighted sum of ratings given by neighbours to Item3

pred_rating(ME,Item3)=
(0.9 * 5) + (0.1 * 1)
0.9 + 0.1
=4.6

    Note: overlapping of rated items required!

Collaborative Filtering Weakness

CF Weakness: Cold start

New users have 0 ratings -->
    Step 1 fails (User Similarity not computable) -->
        No neighbours -->
            Ratings prediction not possible (coverage=0)

Question: how to boostrap a Recommender System for a new user?

Usual solution for bootstrapping

Disadvantages: annoying (users is asked to "work" without an immediate benefit), slow

Our proposed solution for bootstrapping

Advantages: possibly fast (one friend is enough, she can be the user who invited you in the community/system)

Our proposed solution for bootstrapping

We propose to change Step 1 of CF RS

   from "Find users similar to user ME"

        to "Find users trustable by user ME"

Our proposed solution for bootstrapping

Trust-aware RSs

Trust-aware RSs

But what does "trustable" mean?

Users trusted explicitly are neighbours (Kate is neighbour of Mary), and unknown users?

Trust Networks

Trust network: aggregate of all the trust statements

Trust Metrics

How much should ME trust "unknown users"?

Trust Metrics use existing edges for predicting values of trust for non-existing edges,


6 degrees of separation (Stanley Milgram, 1967) -> no more "unknown" users

MoleTrust: local trust metric

MoleTrust (MT)

Experimental analysis

Comparison of:

  1. Standard Collaborative Filtering algorithm (using only ratings information)
  2. Trust-aware algorithm (using only trust statements information): MoleTrust propagating trust up to distance 2.

about their performances on users who expressed few information bits (ratings or trust statements). These users are in need of bootstrapping.


On which data?

Epinions.com description

epinions screenshot

Epinions.com users can:

Most meaningful example of real community expressing trust statements and ratings

Epinions.com dataset

    • ~50,000 users,
    • ~140,000 items,
    • ~660,000 ratings,
    • ~500,000 trust statements (only positive).

Large and real world dataset!

How many cold start users?

#users who provided at most x information bits
0 1 2 3 4 5 6 7
#ratings 18.52% 34.22% 42.21% 48.13% 52.83% 56.72% 60.05% 62.98%
#trusts 31.10% 50.24% 59.7% 65.8% 70.18% 73.61% 76.25% 78.23%

A large portion of the users provided few ratings (cold start users)

Boostrapping for cold start users is a real and important issue.

Analysis: Comparable users

CF (using ratings)
MT2 (using trust statements)

# Neighbours found in Step 1 by 2 techniques, compared when using same quantity of information -- Note the difference in Y axis!

Analysis: Comparable users

CF (using ratings)
MT2 (using trust statements)

Why this huge difference? Trust can be propagated (six degrees).

Evaluation of performances of the algorithms

Evaluation: Coverage

Coverage refers to the percentage of hidden ratings that are predictable

For bootstrapping, trust-awareness is much more effective than standard CF (similarity)

Evaluation: Accuracy

Accuracy refers to the error made when creating a prediction (MAE)

Largest coverage of trust-awareness does not cause smaller accuracy.

Conclusions

THE END

Thanks for your attention!

Trust-aware Bootstrapping of Recommender Systems

Trust-aware Bootstrapping of Recommender Systems

Paolo Massa, Paolo Avesani

ITC/IRST, Trento, Italy

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 2.5 License.

.