Abstract
Web search effectiveness is hampered by several factors, including the existence of several similar resources, ambiguous queries posed by users, and the varied profiles and objectives of users who submit the same query. This chapter focuses on recommendation and diversification techniques as possible solutions to these issues. Recommendation systems aim at predicting the level of preferences of users towards some items, with the purpose of suggesting ones they may like, among a set of elements they haven’t considered yet. Diversification is a technique that aims at removing redundancy in search results, with the purpose of reducing the information overload and providing more satisfying and diverse information items to the user. Both techniques, although typically “hidden behind the curtains” to the end user, result in dramatically enhancing user satisfaction with the performance of retrieval systems.
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Ceri, S., Bozzon, A., Brambilla, M., Della Valle, E., Fraternali, P., Quarteroni, S. (2013). Recommendation and Diversification for the Web. In: Web Information Retrieval. Data-Centric Systems and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39314-3_8
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DOI: https://doi.org/10.1007/978-3-642-39314-3_8
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