Abstract
Ranking web documents that are returned by search engines has been one of the active research areas. In fact, ranking is an essential part of information retrieval. Many ranking approaches such as Page Rank came into existence. Recently Learning to Rank (LTR) emerged as an important machine learning technique which is used for effective ranking. LTR exhibits computational intelligence for bringing about high quality web documents against given web query. LTR became an inevitable phenomenon for making a ranking model and presenting web documents. It is widely used by question-answer kind of applications, search engines and recommender systems. LTR methods are developed to deal with huge number of web documents
Highlights
In the information retrieval domain, it is important to know the order or priority of documents while presenting them
SLOLAR is proposed in this chapter. It is evaluated with other Learning to Rank (LTR) algorithms like SOLAR [5] with benchmark datasets known as LETOR [6]
In the recent past many machine learning algorithms came into existence to have better ranking model
Summary
In the information retrieval domain, it is important to know the order or priority of documents while presenting them. This phenomenon is popularly known as ranking which became crucial for effective information dissemination. Most of the ranking models suffer from retraining to build model when new training data arrives. Such algorithms cannot adapt to conditions that show rapid changes. It is evaluated with other LTR algorithms like SOLAR [5] with benchmark datasets known as LETOR [6]
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