Connectionist-based information retrieval

In this paper we suggest the application of connectionist models to information retrieval problems. In particular we propose the application of neural networks to the task of assigning a score to web pages in order to guide the user in navigating the web. Recent extensions of neural models to the processing of structured domains are a natural framework to build oracles capable to score hypertexts. Recursive neural networks can process directed acyclic graphs and thus can be employed to evaluate a hypertext taking into account also information coming from the pages referred by the hyperlinks it contains. Moreover, we can take advantage from the learning algorithms to adapt the scoring process to the user's preferences and habit. We show how different levels of the scoring process can be implemented using connectionist processing: we propose a method to summarize documents using multi-layer neural networks used as autoassociators; we suggest to use recurrent neural networks as predictors for the user's trajectories in the page domain; we explore the application of recursive neural networks to score hypertextual pages with respect to the context they are in.