Rating Inference for Custom Trips from Enriched GPS Traces using Random Forests

Abstract

Trip planning services are employed extensively by users to compute paths between locations for many different use cases, including commuting to work, transportation of goods, and itinerary planning for tourists. In many scenarios, such as planning for a hiking trip, running training, or mountain cycling, it is desirable to provide users with personalized trips according to their preferences. Existing route planning systems for mountain activities recommend user-posted trips, along with ratings w.r.t. the route’s difficulty, condition, or enjoyment it provides. However, users often want to define a specific trip by choosing the segments/trails they want to follow. Existing systems do not provide a rating for such trips, thus suffering from the cold-start problem. Also, the efforts to automatically infer such a rating have been limited. In this paper, we study the problem of inferring ratings for custom trips. We propose a machine-learning framework that encodes various rated trip features and employs random forest classifiers to infer ratings. We conduct feature engineering to encode information regarding a) trip location, b) trip elevation profile, c) closeness to points of interest, and d) closeness to locations of geotagged photos. Finally, we present the results of an ablation study on two real-world data sets and five different ratings. We evaluate the efficiency of our proposed approach and the effect each feature has on the rating inference accuracy.