Research on information filtration based on genetic algorithm

The technology of information filtering may help the people to pick out the interested information and shield the unnecessary information. Facing the new challenge of the real-time online network information filtration, the technology of the adaptive information filtering appears to be very important in this case. In aspects of the self-learning of user template for adaptive information filtering, with regard to the problem that the initial information pushing to the user having a high correlation but being sparse, this article uses the course of the adaptive profile self-learning based on genetic algorithm for these reasons. Through carrying on the genetic optimization to the information of pseudo-relevance feedback of the system and choosing the most superior feature information into the Rocchio module as the centroid of positive examples, thus realize the adaptive study and renewed the user profile. According to the experimental result, this method has shielded the information sparsity of the pseudo-relevance feedback and the misleading of the feature ambiguity effectively to improve the filtering quality of the adaptive information filtering system.