The Role of User Location in Personalisation of Information Retrieval Systems

Day - Time: 23 July 2014, h.15:00
Place: Area della Ricerca CNR di Pisa - Room: C-29
  • Andrey Rikitianskly (Università della Svizzera Italiana)

Raffaele Perego


Information Retrieval (IR) systems aim at assisting user in finding information among huge variety of resources available on the Web. While traditional IR systems characterized by "one size fits all" approach provide the same list of results for the same query submitted by different users, personalised IR systems tailor search results to a particular user based on his/her interests. However, userâ??s preferences are heterogeneous and changing dramatically in different situations. Information about the environment surrounding user, or userâ??s context, can be usefully exploited to improve search effectiveness. The goal of my research is to investigate the benefit of using contextual information for personalisation of IR systems. In particular, I studied how to leverage userâ??s location to personalise recommender systems and mobile search. In topic of recommender systems, I investigated the problem of suggesting contextually relevant places to a user visiting a new city based on his/her preferences and the location of the city. Based on TREC Contextual Suggestion track evaluations I demonstrated that my system not only significantly outperforms a baseline method but also performs very well in comparison to other runs submitted to the track, managing to achieve the best results in nearly half of all test contexts.
In mobile search area, I proposed a novel approach for location-based personalization of mobile query auto-completion (QAC). Experiments on large-scale mobile query logs showed significant improvements of proposed technique compared to the state-of-the-art QAC approach based on each userâ??s short- and long-term search histories.
The results for both proposed IR systems demonstrated that the location of the user plays an important role for personalization of QAC as well as for place recommender systems. The next step of my research might be devoted to further aspects of geographical context for mobile search. In particular, it would be useful to take into account metadata about organizations/places near userâ??s location. Another possible direction is to investigate a mechanism for predicting the possible gain from location-based personalization before applying it.