The key data that drives most recommendation systems is user behavior data. There are two main types of user behavior data: implicit user feedback data and explicit user feedback data. Explicit user feedback data primarily includes user voting data. Implicit feedback data includes information on users watching, browsing, searching, etc. Explicit feedback data can show a user’s preference on a show explicitly, but implicit feedback data cannot. For example, if a user bought a product at Amazon and gave a five-star rating, it means user is satisfied with the product. If user gave a vote one-star, obviously user does not like. 

 

Architecture

There are many different types of recommendation algorithms, and perhaps the most famous algorithm is collaborative filtering (CF). CF relies on user behavior data, and its main idea is to predict user preferences by analyzing their behaviors. There are two types of CF methods: user-based CF (UserCF) and item-based CF (ItemCF). UserCF assumes that a user will prefer items which are liked by other users who have similar preferences to that user. ItemCF assumes that a user will prefer items similar to the assets he or she preferred previously. ItemCF is widely used by many others (for example, Amazon and Netflix), as it has two main advantages. Firstly, it is suitable for sites where there are a lot more users than items. Secondly, ItemCF could easily explain recommendations given users’ historical behaviors. For example, if you have bought a shampoo like sebamed at Amazon, we will recommend the most popular shampoo to you and tell you that we recommend this because you have bought semamed. 

On-line Achitecture

User profile builder

　　Based on user's history data

Raw recommendation

Filter

Ranking

Explanation

 

 

 

 

 

 

Off-line Achitecture

Data center

Related table generator

Topic model

Feedback analyzer

Report

 

 

 

 

 

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