Context-Driven Tour Planning Service: An Approach Based on Synthetic Coordinates Recommendation

The paper presents a hybrid context/model-based tour planning service aimed at recommendation generation by providing the tourists the sequence of attractions that are more interesting for him/her based on previous activity with the service. The service is developed based on SCoR recommender system that is aimed at recommendation generation based on calculating the synthetic coordinate between tourists of the service in according with their ratings. SCoR is a model-based collaborative filtering algorithm, constructing a model based on the user's personal ratings as well as exploiting collaborative information from the ratings of the rest of the users. One of the main advantages of SCoR's model is its ability to incorporate additional training information (new ratings) without having to perform the training process from the beginning. The prototype has been implemented for Android-based smartphone and has been evaluated for St. Petersburg city. For the evaluation the attraction database has been formed that includes attraction location information from OpenStreeMaps platform, location description and media from Wikipedia, and ratings from Google Place.