Title :
A hybrid context aware system for tourist guidance based on collaborative filtering
Author :
Fenza, G. ; Fischetti, E. ; Fumo, D. ; Loia, V.
Author_Institution :
Dept. of Comput. Sci. & CORISA (Consorzio Ricerca Sist. ad Agenti), Univ. of Salerno, Salerno, Italy
Abstract :
In the area of ambient intelligence there is a need to address user needs according with context features. Recently, the synergy between context aware computing and collaborative filtering is leading to enhance recommender systems with capabilities always nearer to user needs. Specifically, in the domain of tourism it is useful to proactively suggest right sets of attractive locations, events and so on. This work defines a context aware recommender system aimed at suggesting pertinent points of interest (POIs) to tourists. In particular, the approach is strongly based on the synergy between soft computing and data mining techniques. The general framework integrates user profiles, history of social networking and POIs data. Then by defining collaborative filtering approach on the history meaningful POIs are extracted. Indeed, soft computing techniques are mainly applied in order to support activity of unsupervised users and POIs classification. On the other hand, data mining techniques are exploited in order to extract rules able to associate user profile and context features with an eligible set of recommendable POIs. Experimental results show performance in terms of recommendations accuracy.
Keywords :
data mining; groupware; recommender systems; travel industry; ubiquitous computing; ambient intelligence; collaborative filtering; context aware computing; context aware recommender system; context features; data mining; hybrid context aware system; recommender systems; social networking; soft computing; tourism domain; tourist guidance; user profiles; Algorithm design and analysis; Association rules; Clustering algorithms; Collaboration; Context; Recommender systems; context-awareness; data mining; fuzzy data analysis; points of interest; recommender system;
Conference_Titel :
Fuzzy Systems (FUZZ), 2011 IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-7315-1
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2011.6007604