An ontology can be seen as an explicit description of the concepts and relationships that exist in a domain. In this paper, we address the problem of building an interest ontology and predicting potential friendship relations between users in the social network Live Journal, using features constructed based on the interest ontology. Previous work has shown that the accuracy of predicting friendship links in this network is very low if simply interests common to two users are used as features and no network graph features are considered. Thus, our goal is to organize users ’ interests in an ontology (specifically, a concept hierarchy) and to use the semantics captured by this ontology to improve the performance of learning algorithms at predicting if two users can be friends. We have designed and implemented a hybrid clustering algorithm, which combines hierarchical agglomerative and divisive clustering paradigms, and automatically builds the interest ontology. Furthermore, we have explored the use of this ontology to construct interest-based features and shown that the resulting features improve the performance of various classifiers for predicting friendship links.

Abstract. The success of personal information agents depends on their capacity to both identify relevant information for users and proactively recommend context-relevant information. In this paper, we propose an approach to enable proactive context-aware recommendation based on the knowledge of both user interests and browsing patterns. The proposed approach analyzes the browsing behavior of users to derive a semantically enhanced context that points out the information which is likely to be relevant for a user according to its current activities. 1

Abstract. Nowadays, organizational members manage the huge amount of digital documents that they exploit at work. To do that, they organize documents into individual hierarchies. Actually, these documents are really parts of a company’s capital as they reflect past experiences, present competences and impending expertise. Unfortunately, even if corporate documents represent high value-added material, they still mostly remain unknown from the organization as a whole. That is the reason why this paper proposes to build a unified view of corporate documents. Our approach is complementary to current content-based ones because it relies on an original metrics related to documents usage within an organization. 1

For personal information agents, user profiles have to represent user interests and preferences in order to satisfy long-term information needs. An implicit assumption in user-profiling is the existence of persistent interests which, however, might suffer some changes over time. Each time the interests of a user change, his profile becomes inaccurate and the predictive quality decreases. Adaptation of user profiles is, therefore, an essential requirement for personal agents that need to be capable of adjusting their behavior quickly in order to shorten the period of reduced predictive quality. In this paper, a user-profiling technique named WebProfiler, which learns a hierarchical representation of user interests using conceptual clustering, is augmented with an adaptation strategy based on relevance feedback and time-based forgetting in order to deal with drifting interests. We empirically evaluate the performance of this strategy by analyzing its behavior on multiple scenarios of interest drifts and shifts. 1.

Interface agents are computer programmes that provide assistance to users dealing with computer-based applications. The introduction of agents to user interfaces caused the exploration of new metaphors to enhance user ability to directly manipulate interfaces. In this regard, mixed-initiative interaction refers to a flexible interaction strategy in which agents contribute with users by providing suitable information at the most appropriate time. Mixed-initiative approaches promise to dramatically enhance human--computer interaction by allowing agents to resemble human assistants. In this paper, we report a study on how the interaction metaphor can affect the user perception of agent capabilities and, in turn, the final success of agents.

LiveJournal is a social network journal service with focus on user interactions. As for many other online social networks, predicting potential friendships in the Live-Journal network is a problem of great practical interest. Previous work has shown that graph features extracted from the graph associated with the network are good predictors for friendship links. However, contrary to the intuition, user data (e.g., interests shared by two users) does not always improve the predictions obtained with graph features alone. This could be due to the fact that features constructed from a large number of user declared interests cannot capture the implicit semantic of the interests. To test this hypothesis, we use a clustering approach to build an interest ontology, and explore the ability of the ontology to improve the performance of learning algorithms at predicting friendship links, when interest-based features are used alone or in combination with graph-based features. The results show that ontology-based features can help improve the performance of several machine learning classifiers (in particular, random forest classifiers) at the task of predicting links in the LiveJournal social network.