1. Item-to-Item Recommender Network Optimization
By: Chad Adams

2. Contents Introduction Recommender Systems Item-to-Item recommendation
Related Work
Project Overview and Conclusion

3. Introduction
Can the drawbacks to the Item-to-Item approach for recommender systems be reduced?
Through altering the recommendation criteria new items will be recommended.
Can the existing meta-data be used to accomplish this or would new data be needed?
A hybrid system with elements from other studies may provide a possible solution.

4. Recommender Systems
A Recommender system is a machine learning algorithm designed to recommend things to users (products, services, news, other users, etc.)
They act as a search aid for users to sift through vast amounts of data to find what interests them by showing them things that the system has deemed noteworthy for that user.

5. Recommender Systems
There are three main types of recommender systems: Collaborative filtering, Content-based filtering, and Hybrid.
Collaborative methods use a large amount of data on user behaviour to predict their interests. Facebook, Amazon, and Linkedin use this method.
Has the downsides of Cold Start, Scalability, and Sparsity.

6. Recommender Systems
This method gathers data in two different ways explicit and implicit.
Explicit requires user involvement while Implicit gathers data that the user doesn't control.

7. Recommender Systems
Content-based filtering generally uses a combination of keywords, explicit and implicit user preferences, and other descriptive data of the items to recommend similar items to ones the user has liked in the past.
The main methods use either a simple average of the item vector or can become more sophisticated with artificial neural nets, Bayesian Classifiers, cluster analysis, and decision trees.

8. Recommender Systems
The main drawback of this method is its ability to learn user preferences. The method is able to predict interest better the more constrained the content is smaller, but becomes less so the more the constraints are relaxed.
IMDB and Pandora Radio are examples of sites that use Content-based filtering.

9. Recommender Systems
The final type is the Hybrid recommender system, which unsuprisingly, uses a combination of other methods.
Netflix is a good example of a combination of collaborative filtering, comparing the watching and searching habits of similar users, with content-based filtering, recommending movies that share characteristics with others the user has liked.

10. Item-to-Item recommendation
Item-to-Item recommendation is a Collaborative filtering based approach.
It finds similarities between items in the system and recommends them to users based on that individuals interests.
This approach has the benifit of having a reduced network size versus finding user similarities and matching them to items similar users like.

11. Item-to-Item recommendation
But the approach tends to not get much diversity or suprise in recommendations.

12. Item-to-Item recommendation
While this works great for certain items.
But this does not lend itself well to others.

13. Related Work - Finding Community Structure in very large networks by: A. Clauset, M. E. J. Newman, and C. Moore
Introduces idea of Modularity for finding communities in networks.
The goal is to maximize the Modularity for a network which isolates the communities within the network and their bridges to other communities.

14. Related Work
Q = modularity, values of Q > 0.3 tend to indicate a significant community structure in the network, less than 0.3 tends to indicate a more random graph.
The fraction of edges that belong to a community isn't entirely useful, but if you subtract the expected value in a random graph you get Modularity.

15. Related Work Most are satalite communities with a few hubs.
Recommender systems have trouble jumping communities.

16. Related Work Modularity is implied to have only one peak.
The distribution of community size seems to follow a power law.

17. Related Work - ‘Knowing me, knowing you’ — using profiles and social networking to improve recommender systems by P Bonhard and M A Sasse
Studies the advice seeking habits of users.
One factor is the trust users have in the one giving advice.
Another is the risk buying an item imposes.
These factors can be exploited to make more meaningful recommendations.

18. Related Work

19. Related Work
Profile similarity and rating overlap are valued slightly higher than familiarity.

20. Project Overview
Item-to-Item approach has diversity issues. Produces similar or obvious recommendations. While useful for certain items or tasks, can be unhelpful for others.
Could modularity be used to find item communities and, using an algorithm, create bridges to increase diversity in recommendations?
Can the helpfulness of positive or negative reviews be used to filter some recommendations?

21. Conclusion
Recommendation systems help users search and filter large data.
The Item-to-Item approach has a speed advantage and aids in browsing but has divirsity issues.
Modularity can be used to find communities in the network.
Advice seeking patterns can be useful in aiding recommendation systems.