An approach based on Combination of Features for automatic news retrieval

TL;DR

This paper introduces a new feature combination approach for automatic news retrieval that enhances ranking quality and efficiency by analyzing keywords and document relevance.

Contribution

The paper proposes a novel Combination of Features (CoF) method and a dataset for improved news retrieval and ranking accuracy.

Findings

Improved ranking quality using the CoF approach

Enhanced retrieval efficiency through feature combination

Effective keyword-based document analysis

Abstract

Nowadays, according to the increasingly increasing information, the importance of its presentation is also increasing. The internet has become one of the main sources of information for users and their favorite topics. It also provides access to more information. Understanding this information is very important for providing the best set of information resources for users. Content providers now need a precise and efficient way to retrieve news with the least human help. Data mining has led to the emergence of new methods for detecting related and unrelated documents. Although the conceptual relationship between documents may be negligible, it is important to provide useful information and relevant content to users. In this paper, a new approach based on the Combination of Features (CoF) for information retrieval operations is introduced. Along with introducing this new approach, we proposed a dataset by identifying the most commonly used keywords in documents and using the most appropriate documents to help them with the abundance of vocabulary. Then, using the proposed approach, techniques of text categorization, evaluation criteria and ranking algorithms, the data were analyzed and examined. The evaluation results show that using the combination of features approach improves the quality and effects on efficient ranking.