Abdulmohsen Algarni

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It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and(More)
It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of large scale terms and data patterns. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the(More)
Relevance Feedback (RF) has been proven very effective for improving retrieval accuracy. Adaptive information filtering (AIF) technology has benefited from the improvements achieved in all the tasks involved over the last decades. A difficult problem in AIF has been how to update the system with new feedback efficiently and effectively. In current feedback(More)
User relevance feedback is usually utilized by Web systems to interpret user information needs and retrieve effective results for users. However, how to discover useful knowledge in user relevance feedback and how to wisely use the discovered knowledge are two critical problems. However, understanding what makes an individual document good or bad for(More)
With the overwhelming increase in the amount of texts on the web, it is almost impossible for people to keep abreast of up-to-date information. Text mining is a process by which interesting information is derived from text through the discovery of patterns and trends. Text mining algorithms are used to guarantee the quality of extracted knowledge. However,(More)
Faculty of Science and Technology Doctor of Philosophy by Abdulmohsen Algarni It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based(More)
Over the years, people have often held the hypothesis that negative feedback should be very useful for largely improving the performance of information filtering systems; however, we have not obtained very effective models to support this hypothesis. This paper, proposes an effective model that use negative relevance feedback based on a pattern mining(More)
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(More)