Adaptive Highlighting Of Links To Assist Surfing On The Internet

The largest source of information is the WWW. Gathering of novel information from this network constitutes a real challenge for artificial intelligence (AI) methods. Large search engines do not offer a satisfactory solution, their indexing cycle is long and creates a time lag of about one month. Moreover, sometimes search engines offer a huge amount of documents, which is hard to constrain and to increase the ratio of relevant information. A novel AI-assisted surfing method, which highlights links during surfing is studied here. The method makes use of (i) ‘experts’, i.e. pre-trained classifiers, forming the long-term memory of the system, (ii) relative values of experts and value estimation of documents based on recent choices of the users. Value estimation adapts fast and forms the short-term memory of the system. (iii) Neighboring documents are downloaded, their values are estimated and valuable links are highlighted. Efficiency of the idea is tested on an artificially generated sample set, on a downloaded portion of the Internet and in real Internet searches using different models of the user. All experiments show that surfing based filtering can efficiently highlight 10-20% of the documents in about 5 to 10 steps, or less.