Ciaramella, Alessandro (2011) Situation awareness in mobile recommendation systems. Advisor: Lazzerini, Prof. Beatrice. pp. 117. [IMT PhD Thesis]
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Abstract
Nowadays, a huge quantity of resources for mobile users is made available on the most important marketplaces. Further, handheld devices can accommodate plenty of these resources, such as applications, documents and web pages, locally. Thus, to search for resources suitable for specific circumstances often requires a considerable effort and rarely brings to a completely satisfactory result. Moreover, mobile users are likely to devote only partial attention and time to the devices while using them, because the primary task is interacting with the reality, e.g. moving, chatting or even driving a car. A tool able to recommend suitable resources at the right time in each situation would be of great help for the mobile users and would make the use of the handheld devices less boring and more attractive. To this aim, new levels of granularity, together with some degree of selfawareness, are needed to assist mobile users in managing and using resources. Situation awareness can provide a powerful mechanism to identify the user needs at a certain time, enhancing the device usage. However, determining the correct user situation is not a trivial task, due to imperfect domain knowledge, uncertainty in data, and changing user behaviors. In this thesis, we propose a situation-aware resource recommender, which helps mobile users to timely locate resources proactively. Situations are determined by a semantic reasoner that exploits domain knowledge expressed in terms of ontologies and semantic rules. This reasoner works in synergy with a fuzzy engine, which is in charge of handling the vagueness of some conditions in the semantic rules, computing a certainty degree for each inferred situation. These degrees are used to rank the situations and consequently to assign a priority to the resources associated with the specific situations. Moreover, in order to adapt the situation recognizer to the specific user, the system collects data during the interaction of the user with the mobile device. This context history is exploited by genetic algorithms to learn user habits and adapt accordingly the meaning of the linguistic values used in the fuzzy engine. The proposed framework is evaluated by means of real case studies concerning resource recommendations, and experimental results show the effectiveness of the approach.
| Item Type: | IMT PhD Thesis |
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| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| PhD Course: | Computer Science and Engineering |
| Identification Number: | 10.6092/imtlucca/e-theses/27 |
| NBN Number: | urn:nbn:it:imtlucca-27063 |
| Date Deposited: | 10 Jul 2012 09:17 |
| URI: | http://e-theses.imtlucca.it/id/eprint/27 |
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