Web Engineering: 11th International Conference, ICWE 2011, by Stefano Ceri, Alessandro Bozzon, Marco Brambilla (auth.),

By Stefano Ceri, Alessandro Bozzon, Marco Brambilla (auth.), Sören Auer, Oscar Díaz, George A. Papadopoulos (eds.)

This e-book constitutes the refereed court cases of the eleventh foreign convention on internet Engineering, held in Paphos, Cyprus, in June 2011. The 22 revised complete papers and 15 revised poster papers awarded including 2 invited lectures have been conscientiously reviewed and chosen from ninety submissions for inclusion within the ebook. The papers themes conceal a large diversity of components, particularly, the Semantic internet, internet companies, Mashups, internet 2.0, internet caliber, net improvement, etc.

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Extra resources for Web Engineering: 11th International Conference, ICWE 2011, Paphos, Cyprus, June 20-24, 2011

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In the last heuristics, we identify the list of all text groups in the parent segment, partitioned by each occurring child segment. If an interleaving situation is encountered, we perform one-to-one assignments between text groups to child segments. We show a fragment of the results produced by the three heuristics below. 2 4 hasLabel(e_320_input,t_322,"Nailsea / Backwell"). hasLabel(e_326_input,t_328,"Portishead / Pill"). hasLabel(e_515_select,t_400,"Min Price"). hasLabel(e_705_select,t_594,"Max Price").

To what extent do these profiles overlap? Can cross-system user modeling support the construction of tag-based profiles and how does it impact the performance of recommender systems in cold-start situations where user profiles are sparse? In this paper we answer these questions and tackle the following research challenge. 30 F. Abel et al. Definition 3 (User modeling challenge). Given a user u, the user modeling strategies have to construct a tag-based profile P (u) so that the performance of tag and resource recommendations in cold-start situations is maximized.

Presented an approach to merge user’s tag clouds from two different social networking websites to generate richer user interest profiles [18], but did not investigate the impact of the generated profiles on personalization. Different from personalization in social tagging systems that targets single systems [16,17], cross-system personalization makes the investments in personalizing a system transferable to other systems. Mehta et al. showed that cross-system personalization makes recommender systems more robust against spam and cold start problems [14].

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