What is Web Personalization? Process of Personalization



Web Personalization is the process of customizing a website to the needs of specific users. It is the process of providing a unique experience to the every user. For example, if a person sitting at India is using Amazon, the person will be showed up the results which are most frequently purchased or viewed by Indians. This is called web Personalization.
Let’s understand, when and how the process of Personalization generated with the help of different generations of the web.


  • Web 1.0.
  • Web 2.0.
  • Web 3.0.

Web 1.0

It is the first generation of the web. It was read only web. In this generation of the web, the users are allowed only to read the information. It was very limited due to the limited resources. It was same like as we read newspaper. In this generation, the users were not able to provide the feedback or comment on an information. Moreover, they couldn’t communicate effectively with the others users.

Web 2.0

This generation of the web removes the disadvantages of the first generation of the web. It is also called as the read and write generation of the web. In this generation, two way communication is possible. for example, blogging, where users can interact with the author of the contents.
But the main disadvantages of this generation are lack of semantic web and lack of Personalization.

Web 3.0

The current web is 3.0. It incorporates the process of Personalization and semantic web. Semantic web is also known as the extension of the web 2.0. In semantic web, a lot of annotations  and semantic tags are used on the web to make the information machine understandable.
You can also understand the Personalization with respect to the web mining and the three techniques of the web mining,

What is Web Mining? What are different techniques of webmining?


Web Personalization is divided into three main phases:
  • Learning
  • Matching
  • Recommendation


In the first phase, information of the user is collected which is further used to personalize the contents of a website. This information may be like the name of the user, age, sex, his past clicked documents etc.
The information can be collected in two ways: Implicit learning and Explicit Learning. In implicit learning, the information is collected without the intervention of the user. In explicit learning, the users are asked to provide the information.


In this phase, the collected information of the user is matched with the contents of the web pages. After matching the users’ information and contents of a web page, most matching and suitable contents are provided to user.
In this phase, many filtering algorithms are used like Collaborative filtering, Content Based Filtering, Rule Based Filtering, Hybrid Filtering.


The last phase is recommendation which provides the best matched items to the user.

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