Personalized Recommendation of Travel Itineraries based on Tourist Interests and Preferences

Travel itinerary recommendation is an important but challenging problem, due to the need to recommend captivating Places-of-Interest (POI) and construct these POIs as a connected itinerary. Another challenge is to personalize these recommended itineraries based on tourist interests and their preferences for starting/ending POIs and time/distance budgets. Our work aims to address these challenges by proposing algorithms to recommend personalized travel itineraries for both individuals and groups of tourists, based on their interest preferences. To determine these interests, we first construct tourists’ past POI visits based on their geo-tagged photos and then build a model of user interests based on their time spent visiting each POI. Experimental evaluation on a Flickr dataset of multiple cities show that our proposed algorithms out-perform various baselines in terms of recall, precision, F1-score and other heuristics-based metrics.

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