Abstract
The World Wide Web (WWW) is a huge information network from which retrieving and organizing quality relevant content remains an open question for mostly all implicit temporal queries, i.e., queries without any date but with an underlying temporal intent. In this research, we aim at studying the temporal nature of any given query by means of web snippets or web query logs. For that purpose, we conducted a set of experiments, which goal is to assess the percentage of web snippets or queries (in query logs) having temporal features, thus checking whether they are a valuable source of data to help on inferring the temporal intent of queries, namely implicit ones. Our results show that web snippets, as opposed to web query logs, are an important source of concentrated information, where time clues often appear. As a consequence, they can be particularly useful to identify and understand "on-the-fly" the implicit temporal nature of queries in the context of ephemeral clustering.

Abstract

Abstract
In this paper we describe an application of recommender systems to team building in a company or organization. The recommender system uses a collaborative filtering model based approach. Recommender models are sets of association rules extracted from the activity log of employees assigned to projects or tasks. Recommendation is performed at two levels: first by recommending a single team element given a partially built team; and second by recommending changes to a completed team. The methodology is applied to a case study with real data. The results are evaluated through experimental tests and one survey to potential users.

Abstract

Abstract
In this paper we describe a platform that enables Web site automation and monitoring. The platform automatically gathers high quality site activity data, both from the server and client sides. Web adapters, such as rec-ommender systems, can be easily plugged into the platform, and take advantage of the up-to-date activity data. The platform also includes a module to support the editor of the site to monitor and assess the effects of automation. We illustrate the features of the platform on a case study, where we show how it can be used to gather information not only to model the behavior of users but also the impact of the personalization mechanism. Copyright © 2008, Association for the Advancement of Artificial Intelligence.

Abstract