Störningsinformation i tågtrafiken : Attityder bland resenärer och tågbolag till störningsinformation och tjänster för sådan information

When booking a trip along the railway through several train operators it is not uncommon that information about possible disruptions along the railway (that can change or cancel the booked trip) are not relayed to the passengers. Today, research on rail traffic in Sweden is limited. It is unclear how satisfied customers are with the quality of the information they receive during their trip (if they get it at all), including with respect to disruptions. Our partners have identified what they believe is a need among train operators, which is a service for disruption information to travelers. In addition to confirming that there is a need for such a service, we have an interest to investigate how such a service might look like and what the users want. Our research has shown that passengers are not satisfied with either the amount of information about disturbances or how often they get it. Along with KnowitBorlänge, we have come up with a proposed solution that uses already existing technologies to create a portal for an efficient way to get the interference information to travelers.

Collaborative Filtering with Data Classification : A Combined Approach to Hotel Recommendation Systems

Recommendation systems aim to help users make decisions more efficiently. The most widely used method in recommendation systems is collaborative filtering, of which, a critical step is to analyze a user's preferences and make recommendations of products or services based on similarity analysis with other users' ratings. However, collaborative filtering is less usable for recommendation facing the "cold start" problem, i.e. few comments being given to products or services. To tackle this problem, we propose an improved method that combines collaborative filtering and data classification. We use hotel recommendation data to test the proposed method. The accuracy of the recommendation is determined by the rankings. Evaluations regarding the accuracies of Top-3 and Top-10 recommendation lists using the 10-fold cross-validation method and ROC curves are conducted. The results show that the Top-3 hotel recommendation list proposed by the combined method has the superiority of the recommendation performance than the Top-10 list under the cold start condition in most of the times.