A pattern mining approach for information filtering systems

Abstract

It is a big challenge to clearly identify the boundary between positive and negative streams for information filtering systems. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on the RCV1 data collection, and substantial experiments show that the proposed approach achieves encouraging performance and the performance is also consistent for adaptive filtering as well.

Design of Cloud Computing Database and Tourism Intelligent Platform Based on Machine Learning

“…If we develop a cloud computing database that gets rid of the original technology, it will be the next development theme. The literature points out that the recommendation system based on artificial intelligence is an information filtering system, which acts as a bridge between people and information [8][9]. According to the information on the screen and the historical data behavior of users, key information is screened from a large amount of information and data, and then accurately recommended to users, reducing the difficulty of users' selection.…”

Relevance Feature Discovery for Text Mining

“…However, it is very difficult to measure the specificity of terms because a term's specificity depends on users' perspectives of their information needs [55]. We proposed the first definition of the specificity in [30], [31], which calculated the specificity score of a term based on its appearance in discovered positive and negative patterns. However, this definition required an iterative algorithm (3 loops) in order to weight terms accurately.…”