  Tour itinerary planning and recommendation are challenging tasks for tourists
in unfamiliar countries. Many tour recommenders only consider broad POI
categories and do not align well with users' preferences and other locational
constraints. We propose an algorithm to recommend personalized tours using
POI-embedding methods, which provides a finer representation of POI types. Our
recommendation algorithm will generate a sequence of POIs that optimizes time
and locational constraints, as well as user's preferences based on past
trajectories from similar tourists. Our tour recommendation algorithm is
modelled as a word embedding model in natural language processing, coupled with
an iterative algorithm for generating itineraries that satisfies time
constraints. Using a Flickr dataset of 4 cities, preliminary experimental
results show that our algorithm is able to recommend a relevant and accurate
itinerary, based on measures of recall, precision and F1-scores.
