Based on a Publish/Subscribe paradigm, Web Syndication formats such as RSS have emerged as a popular means for timely delivery of frequently updated Web content. According to these formats, information publishers provide brief summaries of the content they deliver on the Web, while information consumers subscribe to a number of RSS feeds and get informed about newly published items. The goal of this thesis is to propose a notification system which scales on the Web. To deal with this issue, we should take into account the large number of users on the Web and the high publication rate of items. We propose a keyword-based index for user subscriptions to match it on the fly with incoming items. We study three indexing techniques for user subscriptions. We present analytical models to estimate memory requirements and matching time. We also conduct a thorough experimental evaluation to exhibit the impact of critical workload parameters on these structures. For subscriptions which are never notified, we adapt the indexes to support a partial matching between subscriptions and items. We integrate a diversity and novelty filtering step in our system in order to decrease the number of notified items for short subscriptions. This filtering is based on the set of items already received by the user.
