Content based filtering is one of the most common recommending approaches. It provides recommendation based on items user currently likes or uses. For example, if a user likes the movie Frozen, content based filtering will find movies similar to Frozen according to movie characteristics such as movie category, producers, actors and movie length etc.
“The steps in recommending products or contents to the user in content based filtering are as follows:
Identify the factors which describe and differentiate the products and the factors which might influence whether a user would buy the product or not,
Represent all the products in terms of those factors, descriptors or attributes,
Create a tuple or number vector for each product that represents the strength of each factors for the product,
Start to look at the users and their histories to create a user profile based on their history. It will have the same number of factors and their strength would indicate how much influenced the user is towards that factor,
Recommend the user those products that are nearest to them in terms of those factors.”
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