Month: September 2017

Mention of my work applied machine learning at Hearst on this Google blog post

https://www.blog.google/topics/journalism-news/how-publishers-can-take-advantage-machine-learning/

Have been reading research work for recommendation engine, specifically that can be used to do better news/blog recommendations.

Links on work in this area including open source code.

Fundamental Building Blocks

• Convert a document or paragraph into a vector representation Doc2Vec https://arxiv.org/pdf/1405.4053.pdf
• Using lstm/gru to represent sentences, works better than Doc2Vec for information retrieval tasks. Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval https://arxiv.org/pdf/1502.06922.pdf

Survey

• Survey of Deep Recommendation Engines. Good starting point https://arxiv.org/pdf/1707.07435.pdf

Recommendation Systems

• Google Deep and Wide https://arxiv.org/pdf/1606.07792.pdf
• Multitask Recommender System Using GRU https://arxiv.org/pdf/1609.02116.pdf
• DeepFM, no need for feature engineering as in Google Deep and Wide https://arxiv.org/abs/1703.04247
• Multi-Rate Deep Learning for Temporal Recommendation. Using multiple time scales and user features trains using DSSM (Deep Semantic Structured Model).  http://sonyis.me/paperpdf/spr209-song_sigir16.pdf
• YouTube Recommendation System https://pdfs.semanticscholar.org/bcdb/4da4a05f0e7bc17d1600f3a91a338cd7ffd3.pdf
• Session Based Recommendation System https://arxiv.org/pdf/1511.06939.pdf . Only uses sequence of content, and not the content itself.

Code

• Subreddit recommendation. RNN based, does not use content. https://cole-maclean.github.io/blog/RNN-Based-Subreddit-Recommender-System