  Trip recommendation is a significant and engaging location-based service that
can help new tourists make more customized travel plans. It often attempts to
suggest a sequence of point of interests (POIs) for a user who requests a
personalized travel demand. Conventional methods either leverage the heuristic
algorithms (e.g., dynamic programming) or statistical analysis (e.g., Markov
models) to search or rank a POI sequence. These procedures may fail to capture
the diversity of human needs and transitional regularities. They even provide
recommendations that deviate from tourists' real travel intention when the trip
data is sparse. Although recent deep recursive models (e.g., RNN) are capable
of alleviating these concerns, existing solutions hardly recognize the
practical reality, such as the diversity of tourist demands, uncertainties in
the trip generation, and the complex visiting preference. Inspired by the
advance in deep learning, we introduce a novel self-supervised representation
learning framework for trip recommendation -- SelfTrip, aiming at tackling the
aforementioned challenges. Specifically, we propose a two-step contrastive
learning mechanism concerning the POI representation, as well as trip
representation. Furthermore, we present four trip augmentation methods to
capture the visiting uncertainties in trip planning. We evaluate our SelfTrip
on four real-world datasets, and extensive results demonstrate the promising
gain compared with several cutting-edge benchmarks, e.g., up to 4% and 12% on
F1 and pair-F1, respectively.
