wayGoo recommender system: personalized recommendations for events scheduling, based on static and real-time information
| Autor: | Konstantinos-Georgios Thanos, Stelios C. A. Thomopoulos |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Information retrieval
Computer science Event scheduling User modeling 02 engineering and technology Recommender system computer.software_genre Naive Bayes classifier 020204 information systems 0202 electrical engineering electronic engineering information engineering Collaborative filtering 020201 artificial intelligence & image processing Real-time data Data mining Cluster analysis computer Random variable |
| Zdroj: | Signal Processing, Sensor/Information Fusion, and Target Recognition XXV. |
| ISSN: | 0277-786X |
| Popis: | wayGoo is a fully functional application whose main functionalities include content geolocation, event scheduling, and indoor navigation. However, significant information about events do not reach users’ attention, either because of the size of this information or because some information comes from real – time data sources. The purpose of this work is to facilitate event management operations by prioritizing the presented events, based on users’ interests using both, static and real – time data. Through the wayGoo interface, users select conceptual topics that are interesting for them. These topics constitute a browsing behavior vector which is used for learning users’ interests implicitly, without being intrusive. Then, the system estimates user preferences and return an events list sorted from the most preferred one to the least. User preferences are modeled via a Naive Bayesian Network which consists of: a) the ‘decision’ random variable corresponding to users’ decision on attending an event, b) the ‘distance’ random variable, modeled by a linear regression that estimates the probability that the distance between a user and each event destination is not discouraging, ‘ the seat availability’ random variable, modeled by a linear regression, which estimates the probability that the seat availability is encouraging d) and the ‘relevance’ random variable, modeled by a clustering – based collaborative filtering, which determines the relevance of each event users’ interests. Finally, experimental results show that the proposed system contribute essentially to assisting users in browsing and selecting events to attend. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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