## 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
In detail, the collected information is related to queries and snippets (titles and summaries) of the results. _These profiles are then used to re-rank the search results and the rank-order of the user-examined results before and after re-ranking have been compared_[9]. User profiles are defined by considering the reference concepts from the ODP. Each concept (or category) of the ODP is weighted, where higher values imply major interest for this category. Each weight is assigned by classifying the textual content collected from the user in the right category. This means that the relevant concepts extracted from the information stored by the user are classified in the ODP hierarchy. This procedure produces a list of concepts with an associated weight that can be updated (or defined) over time after the submission of user's queries, or after the analysis of preferred snippets by the user. The formulae of how to calculate the weight for each concept are given in [66]. The re-ranking phase is performed in two steps. Step (1): for each search result obtained after the evaluation of a user's query, the relevant concepts are extracted and located in the ODP. Furthermore, for each category of the ODP a weight is calculated according to the formula defined in [66]. This process allows to define the so-called _Web profile_ produced by the classification of each Web result. Then, the conceptual match between the Web profile of the \(j-th\) result (_i.e._, \(res_{j}\)), and the user profile (_i.e._, \(user_{i}\)) is defined as: \(conceptual\_match(user_{i},res_{j})=\sum_{k=1}^{N}cwt_{ik}\cdot cwt_{jk}\), where \(cwt_{ik}\) is the weight of concept \(k\) in the user profile \(i\), \(cwt_{jk}\) is the weight of concept \(k\) in the Web profile \(j\), and \(N\) is the number of concepts. This new value is called conceptual rank. Step (2): the final rank of the \(j-th\) result is calculated by combining the conceptual rank with Google's original rank as: \(FinalRank(res_{j})=\alpha\cdot ConceptualRank_{j}+(1-\alpha)\cdot GoogleRank_{j}\), where \(\alpha\) has value between \(0\) and \(1\). If \(\alpha=0\), then the conceptual rank is not considered, and the \(FinalRank\) is equal to the original rank provided by Google. If \(\alpha=1\), then search engine ranking is ignored, and only the conceptual rank is considered. The authors have performed evaluations based on two user profiles (for a same user) by considering two types of information: past user's queries and snippets obtained in response to a user's query, respectively. Evaluations on these user profiles are effective in the same way. In [64] a user profile based on the ODP is used in order to re-rank the Web search results. In this work, a user profile is directly defined by the user. In fact, a user has to select the topics that best fit his/her interests from the ODP. The ODP categories (topics of interest) are organized in a hierarchical way. When a user has chosen the categories of interest, the path of each category is stored in the user profile, _i.e._ all categories until the root. For example, a part of a user profile could be:  
\[Music/Styles/Jazz,\;Music/Instruments/Guitar,\;Music/Artist/Lewis.\]  
Once the user profile has been defined, then the output given after the evaluation of a query is re-ranked by computing a distance between the user profile and each output result. In detail, after the evaluation of a user's query, the obtained results are stored and then compared with the user profile in order to establish which result is most related to the user's interests. For each result the relevant concepts are related to the ODP categories. As a consequence, a result is classified according to the ODP ontology as the user profile. This way the distance between a user profile and a result, \(Dist_{j}\), is performed by analyzing their distance in the ODP structure. The ODP can be considered as a tree, and its categories/topics can be nodes.