Lecture-E7 - Lecture E Collaborative Filtering via...

Lecture E
Collaborative Filtering via
Euclidean Embedding
M. Khoshneshin and W. Street
Proc. of ACM RecSys, pp. 87-94, 2010

Introduction

Recommendation Systems

Suggest items based on user preferences

Recommendation Approaches:

Content-based
•
Items are recommended based on a user profile and product
information

Collaborative Filtering
•
Use similarity to recommend items that were liked by similar users,
i.e., recommendation is based on the rating history of the
system
•
Predict unknown ratings so that users can be given suggestions
based on items with a high expected rating
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Challenges

Existing approaches are more adequate for static settings

Incorporating new data to this models is not a trivial task

Recommendations are based on the best predicted ratings

However, predicting ratings is very computationally expensive in
large databases

Euclidean embedded (EE) method for collaborative filtering

Users and items are embedded in a unified Euclidean space

The distance between a user and an item is inversely proportional
to the rating
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Solution

Euclidean Embedding

Advantages of EE

Is more intuitively understandable for human allowing
useful visualizations

Allows very efficient recommendation queries

Facilitates online implementation requirements
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