Thesaurus enrichment for query expansion in audiovisual archives.

It is common practice in audiovisual archives to disclose documents using metadata from a structured vocabulary or thesaurus. Many of these thesauri have limited or no structure. The objective of this paper is to find out whether retrieval of audiovisual resources from a collection indexed with an in-house thesaurus can be improved by enriching the thesaurus structure. We propose a method to add structure to a thesaurus by anchoring it to an external, semantically richer thesaurus. We investigate the added value of this enrichment for retrieval purposes. We first anchor the thesaurus to an external resource, WordNet. From this anchoring we infer relations between pairs of terms in the thesaurus that were previously unrelated. We employ the enriched thesaurus in a retrieval experiment on a TRECVID 2007 dataset. The results are promising: with simple techniques we are able to enrich a thesaurus in such a way that it adds to retrieval performance. © 2009 The Author(s)

SIRUP: Serendipity In Recommendations via User Perceptions

In this paper, we propose a model to operationalise serendipity in content-based recommender systems. The model, called SIRUP, is inspired by the Silvia's curiosity theory, based on the fundamental theory of Berlyne, aims at (1) measuring the novelty of an item with respect to the user profile, and (2) assessing whether the user is able to manage such level of novelty (coping potential). The novelty of items is calculated with cosine similarities between items, using Linked Open Data paths. The coping potential of users is estimated by measuring the diversity of the items in the user profile. We deployed and evaluated the SIRUP model in a use case with TV recommender using BBC programs dataset. Results show that the SIRUP model allows us to identify serendipitous recommendations, and, at the same time, to have 71% precision