A GP-adaptive web ranking discovery framework based on combinative content and context features

摘要

The problem of ranking is a crucial task in the web information retrieval systems. The dynamic nature of information resources as well as the continuous changes in the information demands of the users has made it very difficult to provide effective methods for data mining and document ranking. Regarding these challenges, in this paper an adaptive ranking algorithm is proposed named GPRank. This algorithm which is a function discovery framework, utilizes the relatively simple features of web documents to provide suitable rankings using a multi-layer/multi-population genetic programming architecture. Experiments done, illustrate that GPRank has better performance in comparison with well-known ranking techniques and also against its full mode edition.