Υβριδική προσαρμοστική μέθοδος πρόβλεψης μεταβαλλόμενων προτιμήσεων χρηστών

Χρησιμοποιούμε το ιστορικό ενός χρήστη που περιέχει την αλληλεπίδραση του με το σύστημα μέσα στον χρόνο και μέσω αυτού προσπαθούμε να βγάλουμε συμπεράσματα για το ποιες είναι οι προτιμήσεις και τα ενδιαφέροντα του. Για να μπορέσουμε να προβλέψουμε με μεγαλύτερη επιτυχία τις προτιμήσεις των χρηστών εξάγουμε πληροφορία από το ιστορικό του κάθε χρήστη ξεχωριστά, το προφίλ όμοιων χρηστών, τα δημογραφικά τους χαρακτηριστικά άλλα και τα δομικά χαρακτηριστικά των αντικειμένων που υπάρχουν στο σύστημα. Η μέθοδος εκτιμά την προτίμηση του χρήστη για κάθε δομικό χαρακτηριστικό ενός αντικειμένου και μέσω αυτών για το ίδιο το αντικείμενο. Οι εκτιμήσεις για τις προτιμήσεις των χαρακτηριστικών γίνονται είτε βάσει των αντικειμένων που περιέχουν το χαρακτηριστικό και για τα οποία γνωρίζουμε την προτίμηση του χρήστη, είτε από την προτίμηση που έχουν εκφράσει όμοιοι χρήστες. Από το ιστορικό του χρήστη εντοπίζουμε τα χαρακτηριστικά που είναι πιο σημαντικά για κάθε πρόβλεψη καθώς και το πως αλλάζουν οι προτιμήσεις που έχουν δοθεί στο παρελθόνWe use the logged interaction between users and the system over the time and we try to detect what users likes and what is interesting for them. We extract content based, collaborative based and demographic based user profiles and we combine these to make estimations about how interesting and significant are the features of the items to any user. The new algorithm uses the content-based, collaborative based and demographic based estimations about item features to predict “rates” for items that are not yet considered. From the log files we extract the statistical significance about item features and past user rates to adapt to the current rates that a user would giv

Stereotype-based versus personal-based filtering rules in information filtering systems

Rule-based information filtering systems maintain user profiles where the profile consists of a set of filtering rules expressing the user’s information filtering policy. Filtering rules may refer to various attributes of the data items subject to the filtering process. In personal rulebased filtering systems, each user has his/her own personal filtering rules. In stereotype rule-based filtering systems, a user is assigned to a group of similar users (his/her stereotype) from which he/she inherits the stereotype’s filtering profile. This study compares the effectiveness of the two alternative rule-based filtering methods: stereotype-based rules versus personal rules. We conducted a comparison between filtering effectiveness when using the personal rules or when using the stereotype-based rules. Although, intuitively, personal filtering rules seem to be more effective because each user has his own tailored rules, our comparative study reveals that stereotype filtering rules yield more effective results. We believe that this is because users find it difficult to evaluate their filtering preferences accurately. The results imply that by using a stereotype it is possible not only to overcome the problem of user effort required to generate a manual rule-based profile, but at the same time even provide a better initial user profile

Knowledge-based product support systems

This research helps bridge the gap between conventional product support, where the support system is considered as a stand-alone application, and the new paradigm of responsive one, where the support system frequently communicates with its environment and reacts to stimuli. This new paradigm would enable product support knowledge to be captured, stored, processed, and updated automatically, being delivered to the users when, where and in the form they need it. The research reported in this thesis first defines Product Support Systems (PRSSs) as electronic means that provide accurate and up-to-date information to the user in a coherent and personalised manner. Product support knowledge is then identified as the integration of product, task, user, and support documentation knowledge. Next, the thesis focuses on an ontology-based model of the structure, relations, and attributes of product support knowledge. In that model product support virtual documentation (PSVD) is presented as an aggregation of Information Objects (IOs) and Information Object Clusters (IOCs). The description of PSVD is followed by an analysis of the relation between IOs, IOCs, and domain knowledge. Then, the thesis builds on the ontology-based representation of product support knowledge and explores the synergy between product support, problem solving, and knowledge engineering. As a result, a structured problem solving approach is introduced that combines case-based adaptation and model-based generation techniques. Based on that approach a knowledge engineering framework for product support systems is developed. A conceptual model of context-aware product support systems that extends the framework is then introduced. The conceptual model includes an ontology-based representation of knowledge related to the users, their activities, the support environment, and the device being used. An approach to semi-automatically integrating design and documentation data is also proposed as part of context-aware product support systems development process.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Knowledge-based product support systems

This research helps bridge the gap between conventional product support, where the support system is considered as a stand-alone application, and the new paradigm of responsive one, where the support system frequently communicates with its environment and reacts to stimuli. This new paradigm would enable product support knowledge to be captured, stored, processed, and updated automatically, being delivered to the users when, where and in the form they need it. The research reported in this thesis first defines Product Support Systems (PRSSs) as electronic means that provide accurate and up-to-date information to the user in a coherent and personalised manner. Product support knowledge is then identified as the integration of product, task, user, and support documentation knowledge. Next, the thesis focuses on an ontology-based model of the structure, relations, and attributes of product support knowledge. In that model product support virtual documentation (PSVD) is presented as an aggregation of Information Objects (IOs) and Information Object Clusters (IOCs). The description of PSVD is followed by an analysis of the relation between IOs, IOCs, and domain knowledge. Then, the thesis builds on the ontology-based representation of product support knowledge and explores the synergy between product support, problem solving, and knowledge engineering. As a result, a structured problem solving approach is introduced that combines case-based adaptation and model-based generation techniques. Based on that approach a knowledge engineering framework for product support systems is developed. A conceptual model of context-aware product support systems that extends the framework is then introduced. The conceptual model includes an ontology-based representation of knowledge related to the users, their activities, the support environment, and the device being used. An approach to semi-automatically integrating design and documentation data is also proposed as part of context-aware product support systems development process