User Preferential Tour Recommendation Based on POI-Embedding Methods
This addresses the challenge for tourists in unfamiliar countries by providing more tailored recommendations, though it appears incremental as it builds on existing embedding and optimization techniques.
The paper tackles the problem of personalized tour itinerary recommendation by using POI-embedding methods to better represent POI types and optimize time, location, and user preferences, with preliminary results on a Flickr dataset showing relevant and accurate recommendations based on recall, precision, and F1-scores.
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.