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Deep Recommender systems - Shibsted, Oslo

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Max Pumperla
Max Pumperla

The document discusses recommender systems and how deep learning techniques can be applied. It provides an overview of traditional recommender approaches like content-based filtering, collaborative filtering, and hybrid systems. It then outlines how deep learning methods such as neural networks, item embeddings, deep matrix factorization, and session-based models can be used to power recommender systems. The document emphasizes starting simple, experimenting with deep learning, knowing the optimization goal, having strong evaluation practices, and bringing in experts to help build and scale systems.

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Max Pumperla Oslo, September 27th 2017
PERSONALISATION AND RECOMMENDATION From algorithm to production
AGENDA 1. About Skymind 2. Fundamentals 2.1. What to recommend? 2.2. What is a recommendation? 2.3. What data can we use? 3. Types of recommenders 3.1. Content-based recommendation 3.2. Collaborative filtering 3.3. Hybrid approaches 4. Evaluation 5. Enter deep learning 5.1. Neural networks 5.2. Item embeddings 5.3. Deep matrix factorization 5.4. Session-based recos 5.5. Combining approaches 6. Summary
1. SKYMIND
Founded 2014 Clients 14 Enterprises 3,500 GH Forks, 7,200 Stars 300,000+ DL4J downloads/mo. Team ~35; mostly engineers; 7 PhDs COMPANY OVERVIEW
Deeplearning4j Build, train, and deploy neural networks on JVM ND4J High performance linear algebra CPU and GPU libraries DEEP LEARNING AT SKYMIND
2. FUNDAMENTALS
WHAT TO RECOMMEND?
WHAT TO RECOMMEND?
WHAT IS A RECOMMENDATION? ● Compute a predictive model from data ● Calculate predictions and rank them ● Suggest items (best, top N) ● Respect business rules and other constraints ● Optimize for metric of choice
WHAT DATA CAN WE USE? INPUT & LABELS Ratings: (user_id, item_id, rating) Events (shopping cart, wish list, page view): (user_id, item_id)
WHAT DATA CAN WE USE? ● Implicit vs. explicit ● Beware: absence of evidence vs evidence of absence ● What do we really want to model for?
3. RECOMMENDER SYSTEMS
CONTENT-BASED RECOS ● Explicitly model features from content and user data ● Might use meta-data ● Build feature vector representation
EXAMPLE: TF-IDF ● Number of documents N ● Term frequency TF: count terms in document ● Document frequency DF: count how of often term is in corpus ● TF-IDF = TF / ( DF / N ) Vector representation of content
EXAMPLE: ITEM-ITEM BASED RECO ● Use TF-IDF to vectorize items ● Build user profile vector by aggregating relevant items ● Measure how close an item is to a user (e.g. cosine similarity) User Item A Item B
COLLABORATIVE FILTERING ● Implicitly model features from user behaviour and interaction data ● “Users who liked A also liked …” Users Users Items Items
EXAMPLE: USER-USER BASED RECO ● How close are my ratings from yours on average? ● e.g. Pearson correlation, k-NN etc. ● Recommend items from users close to you Users Users
EXAMPLE: MATRIX FACTORIZATION ● Decompose User-Item matrix into User and Item matrices ● Different ways to do so ● Creates features for both ● Use this to predict ratings by matrix multiplication Users Items Users Items
HYBRID SYSTEMS ● Combine content-based and collaborative approaches ● Can help overcome weaknesses of both ● Many ways to do so
4. EVALUATION
IS IT WORTH WINNING THE NETFLIX PRIZE? ● Accuracy is just one metric ● Precision vs recall ● Diversity vs top sellers ● Serendipity
HOW TO TEST? ● Use hold-out sets: ○ remove item and see if it gets recommended ○ does not generalize very well ● Use A/B testing: ○ more difficult to do right ○ holy grail of reco systems
5. DEEP LEARNING
NEURAL NETWORKS ● Map inputs to outputs through layers of neurons. ● Brain-inspired? Birds vs airplanes ● Flexible & powerful ● Many successes
DEEP LEARNING ● Powered by data, compute & theory ● Representation learning: remove feature engineering from the equation ● Can model and combine a variety of data types ● Highly applicable to reco systems
ITEM EMBEDDINGS Learnable vectorization User Item A Item B
ITEM EMBEDDINGS ● Structure of documents can be learned instead of imposed ● Very successful applications of this, especially with natural language ● Might discover unforeseen patterns
DEEP MATRIX FACTORIZATION 0 … 0 1 0 … 0 Users 0 … 0 1 0 … 0 Products Embeddings Merge Dense Dense Dense Prediction
DEEP MATRIX FACTORIZATION ● Collaborative filtering with Deep learning ● Learns features for users and items automatically ● Can predict ratings and other outcomes
DEEP SESSION-BASED RECOS ● Utilize sequentiality of events ● Recurrent neural networks for modeling time dependencies ● Challenging, but can prevent pathologies
COMBINING APPROACHES ● Can build complex models from components ● Integrate meta-data, learned features etc. ● Anna Karenina principle
6. SUMMARY
TAKE-AWAYS ● Start simple ● Experiment with Deep Learning ● Know what you want to optimize for ● Have solid A/B testing in place ● Bring experts in to help build, deploy and scale
max@skymind.io twitter: maxpumperla github: maxpumperla
DL4J MODEL IMPORT Store Keras model: Load model and predict:

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Deep Recommender systems - Shibsted, Oslo

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