What is collaborative filtering?
Collaborative filtering involves Behavioural patterns of individual user groups evaluated to infer the interests of individuals. It is a special form of Data mining. This makes explicit user input superfluous.
Collaborative filtering is often used for particularly large amounts of data. Areas of application are in the financial services sector with an integration of financial sources and also in e-commerce and Web 2.0 applications.
The overall goal is to automatically filter user interests. To achieve this, information about behaviour and preferences is continuously collected from as many users as possible. An underlying assumption in collaborative filtering is that two people with the same preferences for similar products will behave identically for other similar products.
How does the algorithm work?
Collaborative filtering often takes place in two steps. First, users are searched for who have the same pattern of behaviour as the active user. Then their behavioural patterns are used to make a prediction for the active user.
Also popular is article-based collaborative filtering. Here, popular articles are presented separately and prominently. A similarity matrix is created and relationships between the articles are determined. Based on this matrix, the preferences of the active user are then derived.
There are other forms of filtering based on implicit observation of user behaviour. Here, the behaviour of a single user is compared with the behaviour of all other users. This data can then be used to accurately predict the user's future behaviour. These and similar technologies are extremely practical for today's users who can no longer keep track of all the offers on the market.
What are forms of collaborative filtering?
There are many different approaches to creating an algorithm for collaborative filtering:
User-based collaborative filtering where a user item matrix is used and similar persons are assigned to the active user. The characteristics are compared with corresponding similarity functions and similar persons are used for further calculations.
On the other hand, there is the item-based collaborative filteringThis is a process in which an independent model is created rather than working directly in the user-item matrix. In this way, different "items" are proposed in the process based on the results of the model.
In addition to these two approaches, there is also the content-based collaborative filtering. This algorithm refers to content-based recommender systems that primarily focus on the content of the object and process the attributes of the interacting entity. Collaborative filters are computed to enrich a generic frequency list with behavioural data.
With the help of Python can neural collaborative filtering which replaces product-based content of the filter with artificial neural networks and uses data-based filters with arbitrary self-learning algorithms.