To My wife Somayeh
نویسنده
چکیده
The information found on the Internet is growing at such a rapid rate that soon methods of searching through text using terms frequencies will not be enough. One stream of thought is devoted to personalizing the search, which means taking into account the specific traits and background of a user when answering that user's query. We believe that user's profile, user's community, and user's context are the three main issues to tackle the problem of the ever growing World Wide Web. The thesis investigates the combination of these three streams of thought. In the first part of this thesis, we develop a PERsonalized and Collaborative Information Retrieval System (called PERCIRS), which makes use of the first two factures (profile and community). PERCIRS creates a ranked list for the query q of the user U based on the documents selected by similar users to U which have been considered as relevant to the similar queries to q. The effectiveness of method for finding the similar users plays an important role in PERCIRS's effectiveness. To this aim, we have proposed three categories of formulas to calculate the similarity between two user profiles: equality-based, similarity-based and graph-based. In order to find the optimal category we proposed two evaluation mechanisms: categorization-based and classification-based. Both of these two mechanisms agreed on the graph-based user profile calculation formulas as the optimal formulas. As PERCIRS is a personalized IR system (due to considering user profiles), it can not be evaluated by Cranfield like evaluation mechanisms (e.g. TREC). Hence in this thesis we propose a new mechanism which enables us to evaluate it with the other classical IR systems such as BM25-Okapi. In the second part, the user's context is used to adapt a found document by PERCIRS to user's preferences. We propose to physically and semantically adapt a given document based on the user's profile and context's profile. A mechanism is also proposed to navigate into the adapted documents according to user's preferences.
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