Interactive itinerary planning

Abstract

Abstract-1 Planning an itinerary when traveling to a city involves substantial effort in choosing Points-of-Interest (POIs), deciding in which order to visit them, and accounting for the time it takes to visit each POI and transit between them. Several online services address different aspects of itinerary planning but none of them provides an interactive interface where users give feedbacks and iteratively construct their itineraries based on personal interests and time budget. In this paper, we formalize interactive itinerary planning as an iterative process where, at each step: (1) the user provides feedback on POIs selected by the system, (2) the system recommends the best itineraries based on all feedback so far, and (3) the system further selects a new set of POIs, with optimal utility, to solicit feedback for, at the next step. This iterative process stops when the user is satisfied with the recommended itinerary. We show that computing an itinerary is NP-complete even for simple itinerary scoring functions, and that POI selection is NP-complete. We develop heuristics and optimizations for a specfic case where the score of an itinerary is proportional to the number of desired POIs it contains. Our extensive experiments show that our algorithms are efficient and return high quality itineraries.

On personalized and sequenced route planning

“…Location based services (LBS) are used in a variety of personal life nowadays, and the usefulness of route planning techniques in the LBS systems have been demonstrated by [1,6,8,14]. With the widespread use of the mobile devises, people can easily access route planning services provided by Google, Bing, Baidu, etc.…”

“…Normally, such kind of services is fully supported online. 1,2,3 However, in many cases, where people arrive in unfamiliar cities, they tend to plan their routes based on categorical constraints. To cater for this, the second category focuses on computing a route that passes by those points according to user specified point of interest (POI) categories.…”

Personalized Trip Recommendation with POI Availability and Uncertain Traveling Time

“…For each city, we randomly pick up 100 users from the testing data, and for each user, we select the top n = 150 unvisited POIs, ranked by their estimated ratings, for trip recommendation. This n is a suggested number in [1]. Even with this restriction, the number of trips that consist of 5 POIs can reach billions, which is certainly infeasible for a brute-force search.…”

“…Given a user u with the source x and the destination y, a departure time T0, a time budget b, and a threshold θ ∈ [0, 1), we want to find an optimal trip route P that maximizes user happiness F (P, u) under the following constraints: (1) The route starts at location x and ends at location y. (2) The route satisfies the POI availability constraint, the time budget constraint and the completion probability constraint.…”

“…[8,17] developed several probabilistic models to generate possible packages by considering cost, season, area, etc. [1] used user feedback to improve results on interactive tour recommendation. None of these works focus on the objective to maximize user's happiness.…”