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Similarity & Recommendation - CWI Scientific Meeting - Sep 27th, 2013

Arjen de Vries
Arjen de Vries
Arjen de VriesCo-founder at Spinque

Recommender systems aim to predict the content that a user would like based on observations of the online behaviour of its users. Research in the Information Access group addresses different aspects of this problem, varying from how to measure recommendation results, how recommender systems relate to information retrieval models, and how to build effective recommender systems (note: last Friday, we won the ACM RecSys 2013 News Recommender Systems challenge). We would like to develop a general methodology to diagnose weaknesses and strengths of recommender systems. In this talk, I discuss the initial results of an analysis of the core component of collaborative filtering recommenders: the similarity metric used to find the most similar users (neighbours) that will provide the basis for the recommendation to be made. The purpose is to shed light on the question why certain user similarity metrics have been found to perform better than others. We have studied statistics computed over the distance distribution in the neighbourhood as well as properties of the nearest neighbour graph. The features identified correlate strongly with measured prediction performance - however, we have not yet discovered how to deploy this knowledge to actually improve recommendations made.

Similarity & Recommendation - CWI Scientific Meeting - Sep 27th, 2013

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Similarity & Recommendation
Arjen P. de Vries
arjen@cwi.nl
CWI Scientific Meeting
September 27th 2013
Recommendation
• Informally:
– Search for information “without a query”
• Three types:
– Content-based recommendation
– Collaborative filtering (CF)
• Memory-based
• Model-based
– Hybrid approaches
Recommendation
• Informally:
– Search for information “without a query”
• Three types:
– Content-based recommendation
– Collaborative filtering
• Memory-based
• Model-based
– Hybrid approaches
Today’s focus!
Collaborative Filtering
• Collaborative filtering (originally introduced by
Patti Maes as “social information filtering”)
1. Compare user judgments
2. Recommend differences between
similar users
• Leading principle:
People’s tastes are not randomly
distributed
–A.k.a. “You are what you buy”
Collaborative Filtering
• Benefits over content-based approach
– Overcomes problems with finding suitable
features to represent e.g. art, music
– Serendipity
– Implicit mechanism for qualitative aspects like
style
• Problems: large groups, broad domains
Context
• Recommender systems
– Users interact (rate, purchase, click) with items
Ad

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Similarity & Recommendation - CWI Scientific Meeting - Sep 27th, 2013

Editor's Notes

  1. This is the target user, or the user we want to present recommendations to
  2. It is important to consider the preferences of the rest of the users in the system
  3. Of all the users
  4. The final goal of the system is to detect new items the user may like
  5. One point for each fold