Enhanced information retrieval using domain-specific recommender models

The objective of an information retrieval (IR) system is to retrieve relevant items which meet a user information need. There is currently significant interest in personalized IR which seeks to improve IR effectiveness by incorporating a model of the user’s interests. However, in some situations there may be no opportunity to learn about the interests of a specific user on a certain topic. In our work, we propose an IR approach which combines a recommender algorithm with IR methods to improve retrieval for domains where the system has no opportunity to learn prior information about the user’s knowledge of a domain for which they have not previously entered a query. We use search data from other previous users interested in the same topic to build a recommender model for this topic. When a user enters a query on a topic, new to this user, an appropriate recommender model is selected and used to predict a ranking which the user may find interesting based on the behaviour of previous users with similar queries. The recommender output is integrated with a standard IR method in a weighted linear combination to provide a final result for the user. Experiments using the INEX 2009 data collection with a simulated recommender training set show that our approach can improve on a baseline IR system

Advance BPEL execution adaptation using QoS parameters and collaborative filtering techniques

Στην παρούσα εργασία, προτείνονται πλαίσια που περιλαμβάνουν την προσαρμογή της εκτέλεσης σεναρίων BPEL σε πραγματικό χρόνο. Η προσαρμογή βασίζεται (α) σε χαρακτηριστικά ποιότητας υπηρεσίας των διαθέσιμων παρεχόμενων υπηρεσιών διαδικτύου (β) σε πολιτικές ποιότητας υπηρεσίας που καθορίζονται από τους χρήστες και (γ) σε τεχνικές συνεργατικού φιλτραρίσματος, επιτρέποντας στους πελάτες να εκλεπτύνουν περαιτέρω τη διαδικασία προσαρμογής, εξετάζοντας τις επιλογές υπηρεσιών που έγιναν από άλλους πελάτες στο παρελθόν.In this thesis, frameworks for providing runtime adaptation for BPEL scenarios are proposed. The adaptation is based on (a) quality of service parameters of available web services (b) quality of service policies specified by users and (c) collaborative filtering techniques, allowing clients to further refine the adaptation process by considering service selections made by other clients