#409 overall
Recommender Systems Specialization
Coursera
1 recommender · 1 thread · recommended in 1 year, 2016 · score 1.0
A Coursera specialization covering how recommender systems work, including the main approaches and how to evaluate different models. It is aimed at learners who want to build recommendation engines.
Commenters point to it as a good dedicated course on recommendation systems and note that it covers model evaluation well, which they say many algorithm-focused posts skip.
Machine Learning & AIintermediate#recommender systems#collaborative filtering#model evaluation#coursera
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20062026
There is a good course on coursera just for recommendation systems. Evaluation of different models is something many algorithm oriented posts don't talk about but is covered well in this course.
There are basically 3 types of recommender engines:
Content Based: If you can represent your products as a vector, you can have a distance between each product, then you have a item-item recommendation. You can use all kinds of embedding to achieve this results, some techniques that we tried are word2vec embedding of user navigation, auto encoding of features using neural networks, dimensionality reduction with PCA, ALS, etc. There are lots of libs for solving these problems as is a very studied field, usually numpy and for finding the neighbors we use ann from scikitlearn, because if you have millions of items, you cant just find the distance between all the pairs.
Collaborative - Filtering, here you use the pairs of behavior of the users, <user, item, ranking>. There is a surprise lib in python that works well, you have the MlLib from Spark too, this techniques are called matrix factorization techniques, and also gives you a embedding of the item or the user, and you can apply the techniques of content based to find user-user and item-item recommendations along the user-item recommendations
Hybrid Models: These are the models that use behavior and features of the user an items, LightFM is a good lib that works well, but you can model it with other tools like neural networks ( https://ai.google/research/pubs/pub45530 ).
The challenges are depending on the company, its not the same to recommended small amount of items to large number of users than large number of items to small number users.
There is a whole specialization in coursera that is really good https://www.coursera.org/specializations/recommender-systems