## 4 The Use of Personalized Ontologies for Re-ranking Search Results
### Approaches Defined to Re-rank the Search Results Based on the Use of Personalized Ontologies
From this point of view, the distance is related to two sets of nodes from the topic tree: (1) those representing the user profile (set \(A\)), and (2) those associated to the result (set \(B\)). The distance between these sets is established as the minimum distance between all pairs of nodes given by the Cartesian product \(A\times B\). Thus, the pair of nodes having the minimum distance is considered in order to calculate the \(Dist_{j}\) score to associate to each result. This score is computed by combining the 'Complex distance' (see [64]) between \(a\) and \(b\), and the Google PageRank score of \(b\); where node \(a\) identifies the user profile, and node \(b\) is the result. Once all the results are processed, then it is possible to re-rank them in decreasing order according to their distance value. Several experimental evaluations have been performed by adopting three types of queries (clear query, relatively ambiguous query) in different types of Web searches, such as a personalized ODP search and a personalized Google search by using the Google Directory as reference taxonomy (where Google Directory is a sub-structure of the ODP) instead of the ODP. Thus, by considering the metric previously presented, a user profile is compared with the ODP taxonomy and the Google directory, respectively. In detail, \(17\) users have defined their profile by selecting the categories of interest from the ODP, and then they have defined their queries. The top \(5\) results obtained after the evaluation of a query have been re-ranked by applying the above approach on two reference taxonomies, _i.e._, the ODP and the Google Directory. Thus, for a same query two different rankings of the same result set have been obtained. By using the ODP, good results are obtained for the following types of user's queries: relatively ambiguous query and ambiguous query. In [13] a personalized information retrieval model based on ontological knowledge has been proposed. The ontology, in this case, is built by considering two types of user's interests: persistent and live. Persistent interests are related to the preferences explicitly declared by the user, whereas the live interests are obtained by monitoring the user's actions during a search session. These two methods allow to jointly define the user's profile. To define a persistent interest, a user selects a-priori a set of different topics with a certain degree. The live interest is generated through a constant monitoring of the user's interaction with the system during a search session. As stated by the authors, this last user interest contains less uncertainty because it is based on his/her actions, as long as the monitoring period is sufficient and representative of the user's preferences [13]. The live user profile is defined according to four actions: _keyword-based queries, view document queries, relevance feedback queries, and browsing queries_. _Keyword-based queries_ define a fuzzy set of concepts on the keywords extracted from the user's queries, _view document queries_ define a fuzzy set of concepts extracted from the visited documents by the user, _relevance feedback queries_ denote two sets of documents: one is the set of documents marked by the user as relevant, and the other is the set of documents marked by the user as non-relevant. At the end, _browsing queries_ define a fuzzy set of topics requested for browsing by the user. Hence the ontology is built by jointly considering all these sets from the live interests, plus the preferences assigned directly from the user (_i.e._, persistent interest). The ontology knowledge is then defined with the adoption of a formal methodology founded on fuzzy relational algebra [67, 68]. This formal methodology allows to extract the relevant concepts from the user's interests (persistent and live) and to link them. This way, two concepts are linked, but the semantics of this relation is not known.