Self-tuning Personalized Information Retrieval in an Ontology-Based Framework

Probabilistic Score Normalization for Rank Aggregation

“…query length), the search space, the result set, or other domain-specific factors. Also, we are currently exploring techniques where the coefficients in the linear combination are a function of application-specific variables of the ranking system, such as the uncertainty in the rankings [1].…”

“…To name a few examples, rank aggregation takes place in the combination of multiple criteria for document/query similarity assessment in most search engines; in merging the outputs of different engines for meta-search; in the combination of query-based and preference-based relevance for personalized search [1]; or even in the combination of preferences from multiple users for collaborative retrieval [5]. Both rank-based and score-based aggregation techniques have been explored in prior research on this topic [7].…”

A Multi-Purpose Ontology-Based Approach for Personalised Content Filtering and Retrieval

“…The data handled by these techniques have been rather low-level and simple: document IDs, text keywords and topic categories at most (Jeh & Widom, 2003;Micarelli & Sciarrone, 2004). The recent proposals and achievements towards the enrichment of multimedia content by formal, ontology-based, semantic descriptions open new opportunities for improvement in the personalisation field from a new, richer representational level (Bloehdorn et al, 2005;Castells et al, 2005). We see the introduction of ontology-based technology in the area of personalisation as a promising research direction (Gauch, Chaffee & Preschner, 2003).…”

“…In an ontology-based approach, semantic user preferences may be represented as a vector of weights (numbers from -1 to 1), representing the intensity of the user interest for each concept, being negative values indicative of a dislike for that concept (Castells et al, 2005). Similarly, content is described by a set of weighted concepts (values from 0 to 1, indicating the intensity of relation between the content and the concept) in such a way that users can be related to the content units that make up the search space through the ontology layer (see Figure 1).…”

“…Personalised multimedia access aims at enhancing the retrieval process by complementing explicit user requests with implicit user preferences, to better meet individual user needs (Castells et al, 2005). Automatic user modelling and personalisation has been a thriving area of research for nearly two decades, gaining significant presence in commercial applications around the mid-90's.…”