## 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
The user profile is initialized after a first user's query and updated after the others user's queries. Each time that a user submits a query to the system, the search results are re-ranked according to the user profile defined up to the previous queries. In detail, a search session \(S\) is defined at time \(t+n\) as a sequence of related search activities performed by queries \(\{q^{0},\ldots,q^{t-1},q^{t},q^{t+1},\ldots,q^{t+n}\}\) submitted respectively at time \(\{0,\ldots,t-1,t,t+1,\ldots,t+n\}\). In order to consider queries related to the same search context, the authors have proposed a session boundary recognition method by using the _Kendall_ rank correlation measure that quantifies the correlation between the current user query (e.g., defined at time \(t\)) and the user profile (e.g., defined until time \(t-1\)). A threshold value \(\sigma\) is used in order to define if a query defined at time \(t\) is related to the same context of the previous queries; _i.e._, this is verified only if the value obtained after the calculus of the Kendall measure is greater than \(\sigma\). If the query \(q^{t}\) submitted at time \(t\) is related to the some previous search activities, then the documents of interest returned in response to the user's query are collected. A document is relevant, for a user, if some actions on that document are performed (such as, saving, printing, coping,...). Once all the documents of interest have been identified, \(D^{t}\), then the authors have defined the query context, \(K^{t}\), as the centroid of the documents in \(D^{t}\); namely the weight of each term, \(term_{i}\), in \(K^{t}\) is calculated as \(K^{t}(term_{i})=\frac{1}{|D^{t}|}\cdot\sum_{d_{k}\in D^{t}}w_{term_{idk}}\), where \(w_{term_{idk}}\) is the weight of the \(term_{i}\) in document \(d_{k}\). At this point a vector \(\bar{K^{t}}\) is obtained having the most relevant (weighted-)terms from the documents returned after the evaluation of the query. A user profile is represented as a weighted graph where the nodes are the categories (or concepts) defined in the ODP. This means that concepts are linked by using the taxonomic relationship IS-A. To re-rank the documents according to the user profile, it means that only the concepts that represent the Web pages (for more details see [65]) visited by the user during the previous queries at time \(t-1\) are taken into account. Then, each selected concept from the ODP (_i.e._, \(c_{j}\)) is represented as a single term vector \(\bar{c_{j}}\), and its similarity score with \(\bar{K^{t}}\) is computed as \(score(c_{j})=cos(\bar{c_{j}},\bar{K^{t}}.)\). When the scores of the concepts are updated, the \(D^{t}\) set is analyzed and compared with the user's profile in order to re-rank the documents according to the user interests. For each document, \(d_{k}\), obtained after the evaluation of a query at time \(t\), a weight is computed as: \(W(d_{k})=\alpha\cdot S_{i}(q^{0},d_{k})+(1-\alpha)\cdot S_{c}(d_{k},G^{t})\) for \(0<\alpha<1\), where \(S_{i}\) is the similarity value between the first user's query and the document \(d_{k}\), while \(S_{c}\) is the similarity value between the document \(d_{k}\) and the user profile at time \(t\). Both the similarity values are calculated by adopting the cosine similarity measure. Once the results are analyzed, then this weight is used in order to re-rank them in decreasing order. In several approaches user profiles are created by considering proxy servers (e.g., to capture browsing histories) or desktop bots (e.g., to capture activities on a personal computer). In [9] an approach is proposed which implements a wrapper around the Google search engine. In this work, a user profile is analyzed to identify the user's interests related to a query in order to improve the quality of a user's search. User profiles are created by classifying the information collected during previous search sessions into concepts with the support of a reference hierarchy (_i.e._, the OPD).