We focus on the goal of designing recommender systems (RSs) for sustainable tourism itinerary planning. In particular, we focus on the problem of rearranging tourist flows in order to protect popular destinations from overcrowding, as well as to stimulate the development of less mature destinations by distributing tourists throughout the territory. This topic is quite new in the recommendation literature, although it is well known in the tourism literature. We aim to transfer concepts from the field of tourism to the ifeld of RSs in order to improve tourism development. Our approach is to take into account the goals of all the active stakeholders, and not to focus solely on tourists. Here, we propose a multistakeholder utility model for travel itinerary optimisation. We experimentally show that it is possible to mitigate the aforementioned environmental issues with a slight decrease in user satisfaction utility.
Recommender Systems (RSs) are software tools that provide users with personalised access to
information [
This section illustrates a case study that is a part of an industrial R&D project on tourism
sustainability in an Italian tourism region. The target RS should stimulate various tourism
activities in a village, such as, hiking adventures, as well as other leisure and cultural experiences.
Given a set of POIs, for each arriving visitor the RS must generate a travel itinerary (an
ordered subset of POIs), based on elicited preferences and the amount of time that the visitor
is willing to allocate [
Multistakeholder approach is a general framework for understanding RSs where the end user is
not the sole focus. According to this framework, a recommendation stakeholder is any group or
individual that can afect, or is afected by, the delivery of recommendations to users [
We adopt a utility-based approach and model independently user utility, , and environment utility, , which includes the considered sustainable goals. Utility is computed for a generated itinerary, as the sum of the utilities of the POIs included in the itinerary. In optimisation section we will discuss how we generate a personalised itinerary based on these utility functions.
User utility is a function of tourist preferences and it is predicted by using a content-based
RS [
Environment utility is a function of time and occupancy of the POIs that are selected for an itinerary. This function has the following properties: when POI occupancy is low, the utility encourages the RS to recommend that POI; when the occupancy of a POI is saturated, utility penalises for overcrowding it. We propose a monotonically decreasing function of occupancy, that has a parabolic shape (Figure 1). This function has its maximum at zero occupancy, equals zero when the occupancy is equal to the capacity (10 in the figure), and becomes negative after that. We conjecture that this definition will also improve spatial coverage of the recommended itineraries, that is, will stimulate a more even distribution of visitors over the area.
When a visitor arrives to the recommendation platform he/she will search for an itinerary that will start immediately. The RS finds a personalised itinerary by scheduling a subset of POIs within an expert route taking into account the predicted occupancy of the POIs in the next time slots and geographical constraints. Indeed, when a visitor experience a POI, he/she occupies a certain area and spends a fixed amount of time at that venue. Also, there is a time cost of travelling between two POIs, which we assume is a constant value for any visitor.
For each incoming visitor the RS solves a bunch of similar itinerary planning optimisation
problems, one optimisation problem for each expert-based route, and then choose the solution
that is the best among the solutions found in the diferent expert-based routes. We consider
in more details one of such itinerary planning problem. We maximise under time constraints
(itinerary time should be less than time a visitor is willing to allocate) the cumulative utility of
the generated itinerary, that is, the sum of the utilities of the POIs that constitute the itinerary:
max ∑︁ · [, + · ,()]
1,..., =1
where is an indicator function (equals 1 if the itinerary contains the -th POI and 0 otherwise);
is the total number of POI candidates in the considered expert-based route; , is the visitor
utility for visiting the -th POI; and ,() is the environment utility which depends on the
time and occupancy of the -th POI. The parameter determines the trade-of between the two
objectives. This optimisation problem can be posed as a search for a path of maximum utility on
a graph, where the graph is represented by ordered POIs in one expert route. Our optimisation
algorithm is based on breadth-rfist search graph exploration heuristic, called beam search [
We utilised a two-step recommendation approach in order to simulate the environment of an Italian village. In the first step, we applied a content-based RS to rank for each incoming visitor the available POIs based on visitor’s typology. In the second step, we searched for the best subset of POIs across all available expert routes to maximise the given combination of user and environment utilities. We created 20 geographically distributed POIs throughout the village with random stay times, transition times, and with the same capacity value (10 visitors). We simulated the arrival of 5 types of tourists, where the tourists of one type have the same preferences and receive the same recommendation list by the content-based RS. In our experiment, visitors arrive one after another with a Poisson inter-arrival rate. We ran our simulation at the highest possible tourist arrival rate that our environment can accommodate without POIs overcrowding (assuming we are ignoring tourist preferences). We defined 5 expert routes with a total duration of 5-6 hours each. Key information about the experiment is represented in Table 1.
Simulation results are illustrated in Figure 3. Each point in figure (left) represents user vs. environment (average) recommended itinerary utility for a specific value of the parameter . There is a clear trade-of between user and environment utilities. Figures (right) show how itinerary planning algorithm distributes visitors to diferent POIs during the day, and what occupancy profile for each POI we expect. We analysed three cases: = 0, = 0.2, and = 4. In the first case, RS does not account for POI occupancy issues. In the second case, RS greatly improves environment utility while slightly reducing user utility. In the third case, environmental goals are fully taken into account: RS can satisfy capacity constraints and encourage exploration of unpopular POIs.
Open time from 6 am to 6 pm, inter-arrival rate 1.4 tourists per min 20 POIs, stay time 20-40 min, transition time 5-20 min, capacity 10 visitors at max 5 types of visitors, max journey duration time for each visitor 3 hours 5 expert routes, total duration time 5-6 hours each
The proposed utility-based RS balances conflicting objectives and reduces over recommendation of popular POIs. It helps to promote unpopular POIs and increases spatial coverage by distributing tourists across an area at little cost to user satisfaction. This multistakeholder approach can improve tourism planning when it is necessary to balance the goals of various stakeholders, as a result, there is a chance to increase the sustainability of local tourism.
However, the proposed model has some limitations. We are aware that in real life, our assumptions are not always fulfilled. In particular, there is a chance that visitors will not follow the suggested itinerary, and even if they will, there is uncertainty about when they will start it and how long they will spend at each POI. In addition, we want to improve the optimisation step and search for optimal itineraries over all permutations of POIs, not just among predefined expert-based routes. These limitations will be addressed in future work.