2017 DeepLearningforPersonalizedSear
- (Fawaz et al., 2017) ⇒ Nadia Fawaz, Saurabh Kataria, Benjamin Le, Liang Zhang, and Ganesh Venkataraman. (2017). “Deep Learning for Personalized Search and Recommender Systems.” In: Proceedings of Tutorial at KDD-2017 .
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Abstract
In the area of personalized recommender systems, deep learning has started showing promising advances in recent years. The key to success of deep learning in personalized recommender systems is its ability to learn distributed representations of users’ and items’ attributes in low dimensional dense vector space and combine these to recommend relevant items to users. To address scalability, the implementation of a recommendation system at web scale often leverages components from information retrieval systems, such as inverted indexes where a query is constructed from a user’s attribute and context, learning to rank techniques. Additionally, it relies on machine learning models to predict the relevance of items, such as collaborative filtering. In this tutorial, we present ways to leverage deep learning towards improving recommender system. The tutorial is divided into four parts: (1) In the first part, we will present an overview of concepts in deep learning which are pertinent to recommender systems including sequence modeling, word embedding and named entity recognition. (2) In the second part, we will present how these fundamental building blocks can be used to improve a recommender system at scale. (3) The third part presents a few case studies from large scale recommender systems at LinkedIn and some of the challenges we faced while getting deep learning to work in production.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2017 DeepLearningforPersonalizedSear | Liang Zhang Nadia Fawaz Saurabh Kataria Benjamin Le Ganesh Venkataraman | Deep Learning for Personalized Search and Recommender Systems | 2017 |