Daniel Tunkelang

In the 4th ACM RecSys Workshop on Recommender Systems and the Social Web (in conjunction with RecSys 2012)


 

Abstract

Recommender systems for the social web combine three kinds of signals to relate the subject and object of recommendations: content, connections, and context.

Content comes first - we need to understand what we are recommending and to whom we are recommending it in order to decide whether the recommendation is relevant. Connections supply a social dimension, both as inputs to improve relevance and as social proof to explain the recommendations. Finally, context determines where and when a recommendation is appropriate.

I’ll talk about how we use these three kinds of signals in LinkedIn’s recommender systems, as well as the challenges we see in delivering social recommendations and measuring their relevance.

BiBTeX

@inproceedings{mobasher20124th, title={4th ACM RecSys workshop on recommender systems and the social web}, author={Mobasher, B. and Jannach, D. and Geyer, W. and Hotho, A.}, booktitle={Proceedings of the sixth ACM conference on Recommender systems}, pages={345--346}, year={2012}, organization={ACM} }