Can I categorise machine learning techniques (Bayesian Network, Neural nets etc) into
BACKGROUND
I'm creating a recommender system for my dissertation and i'm currently doing my literature review. I've done an overview of machine learning techniques; neural networks; decision tree; neural network; KNN; collaborative filtering; Bayesian network; support vector machine.
My next section is titled 'Recommendations in Machine Learning'.
QUESTION
Now can I categorise these techniques into the 6 popular types of recommender systems (Collaborative, Content-Based, Demographic, Utility, Knowledge) without using hybrid system? Or should these simply be completely different sections to my overview.
Examples
Can I categorise this recurrent neural network (http://cole-maclean.github.io/blog/files/subreddit-recommender.pdf) as a demographic based recommender system as it makes recommendations based on classes?
Or can I categorise this Bayesian network (http://dl.ifip.org/db/conf/ifip8/confenis2007-2/ZhangL07a.pdf) as a collaborative recommender system as it takes into account all other users and how they perceive items.
BIG thank you to anyone that can help!
I'm creating a recommender system for my dissertation and i'm currently doing my literature review. I've done an overview of machine learning techniques; neural networks; decision tree; neural network; KNN; collaborative filtering; Bayesian network; support vector machine.
My next section is titled 'Recommendations in Machine Learning'.
QUESTION
Now can I categorise these techniques into the 6 popular types of recommender systems (Collaborative, Content-Based, Demographic, Utility, Knowledge) without using hybrid system? Or should these simply be completely different sections to my overview.
Examples
Can I categorise this recurrent neural network (http://cole-maclean.github.io/blog/files/subreddit-recommender.pdf) as a demographic based recommender system as it makes recommendations based on classes?
Or can I categorise this Bayesian network (http://dl.ifip.org/db/conf/ifip8/confenis2007-2/ZhangL07a.pdf) as a collaborative recommender system as it takes into account all other users and how they perceive items.
BIG thank you to anyone that can help!
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