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:

• Like Amazon.
• Based on other users with similar profiles.
• Experimentally better than content-based, but you don't always have many users.

Knowledge-based RS:

• No/little user history.
• Based on domain knowledge.

User-based collaborative recommendation:

• Pearson's correlation coefficient - baseline.
• Imagine millions of users - computing similarities takes a lot of time.
• So ..

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

• Automating typing of DBPedia entities.

Vector space model for LOD

• MATHS.