Diversification for Multi-domain Result Sets

  • Alessandro Bozzon
  • Marco Brambilla
  • Piero Fraternali
  • Marco Tagliasacchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7387)


Multi-domain search answers to queries spanning multiple entities, like “Find a hotel in Milan close to a concert venue, a museum and a good restaurant”, by producing ranked sets of entity combinations that maximize relevance, measured by a function expressing the user’s preferences. Due to the combinatorial nature of results, good entity instances (e.g., five stars hotels) tend to appear repeatedly in top-ranked combinations. To improve the quality of the result set, it is important to balance relevance with diversity, which promotes different, yet almost equally relevant, entities in the top-k combinations. This paper explores two different notions of diversity for multi-domain result sets, compares experimentally alternative algorithms for the trade-off between relevance and diversity, and performs a user study for evaluating the utility of diversification in multi-domain queries.


Greedy Algorithm User Study Relevance Score Total Price Entity Instance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alessandro Bozzon
    • 1
  • Marco Brambilla
    • 1
  • Piero Fraternali
    • 1
  • Marco Tagliasacchi
    • 1
  1. 1.Politecnico di MilanoMilanoItaly

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