[Notes] Tommaso Di Noia at #SSSW2013

Tools and Techniques

Recommender systems

Input: Set of users + set of items + rating matrix.
Problem - given user, predict rating for an item.

In real world, recommendation matrix data is sparse.

Can use hybrid approaches.

Collaborative RS:

Knowledge-based RS:

User-based collaborative recommendation:

Item-based collaborative recommendation:

Focus on items not users.
Compute similarity between each pair of items.
Don't have to compute similarity between items that don't have overlapping ratings.
Cosine similarity / adjusted cosine similarity (taking into account average rating related to a user to eliminate some bias).

Content-based RS:

Based on description of item
and profile of user interests.
Items are described in terms of attributes/features.
Finite set of values associated with features.
Item representation is a vector.
Don't necessarily have complete descriptions of items - just have a 0 in your vector.
Similarity between items:
- Jaccard similarity.
- Cosine similarity and TF-IDF (term frequency - inverse document frequency).
- Batch compute similarities offline, then use similarities to compute ratings on the fly based on user profile.
Predict rate only for N nearest neighbours of items in user profile, that are not in the user profile.
An item is worth rating if more than x of N number of neighbours are within user profile.

Using LOD

To mitigate lack of information/descriptions about concepts/entities.

Recommender systems are usually vertical, but LD lets you easily build a multi-domain recommender system.

To avoid noisy data, you have to filter it before feeding your RS.

Freebase.

Tiapolo

Vector space model for LOD

semantic web tools (1 out of 1)