Recommendation of Heterogeneous Cultural Heritage Objects for the Promotion of Tourism
2. Related Work
3. Handling Heterogeneous Cultural Heritage Data
3.1. A Unified Model for Cultural Heritage Data
3.2. Importing Heterogeneous Data
4. Leveraging Cultural Heritage Data
4.1. Cultural Heritage Open Data Publication
4.2. The Tourist Application and Its Recommendation Mechanism
4.3.1. User Model
4.3.2. Context Model
4.3.3. Itinerary Model
4.4. A Recommendation Algorithm for Itineraries
|Algorithm 1: Itinerary recommendation—pseudocode|
4.4.1. The Scoring Function
- Set of POIs to evaluate (input no. 1)The scoring functions should, of course, receive a list of POIs to be evaluated. Each POI is represented according to the model described in Section 3.2.
- Location (input no. 2)The location is a geographical coordinate corresponding to the position of the last POI added to the itinerary being constructed. It is defined as part of the physical context (Section 4.3.2).
- User preferences (input no. 3)The preferences of the user are composed of thematic and historical preferences. They are described in Section 4.3.1.
- User context (input no. 4)The user context defines the mode of transportation (e.g., driving, cycling), a date, the estimated visit duration, and the group (alone, with kids, with adults, etc.). These elements are described in Section 4.3.2.
- Travel time graph: (input no. 5)The graph contains information about the time required to travel between POIs. It is defined as , where V is a set of POIs and E is a set of paths (edges) linking two POIs. In our model, we consider that each pair of POIs is linked (i.e., this is a complete graph). The values composing these edges are calculated only once, except if a new edge is inserted in the graph. Since we consider three transportation modes in this paper (on foot, cycling, and driving), each edge contains three values.Figure 12 illustrates the travel time graph between POIs. Each edge contains a triplet of values (, , ) where represents on foot travel time between and , represents cycling travel time between and , and represents driving travel time between and .
- Social graph: (input no. 6)The graph models the relevance of POIs according to the whole set of users and the relevance of a particular sequence of POIs (pheromone trails). This graph is defined as , where V is the set of POIs and E is the set of edges linking two POIs.Figure 13 illustrates the information about the pheromone trails. Each vertex (POI) has a list of values defining its score for each user. That is, this list is composed of a tuple (score of for user u) and (identity of user u who visited ).Each edge has a value representing the intensity of the link between two POIs. This intensity is defined in terms of past interactions with the set of users of the system and the two POIs. We adopt the hypothesis that if two POIs are frequently visited successively, there is a strong link between them (according to the users’ preferences). This modelling choice inserts characteristics of collaborative filtering algorithms in our approach. Moreover, we also increase the score of a POI for a user if other users like it. This hypothesis is implemented through the incorporation of an ant colony algorithm in our solution. We consider that each user is an ant and that every time an ant passes from one POI to another, a specific quantity of pheromone is left in the path. Whenever the amount of pheromone reaches a certain amount, it means a strong link between two POIs exist. Figure 13 presents the pheromone in terms of edges. The bolder these edges are, the stronger is the pheromone trail left between the two edges.Here, one of the main advantages of using ant colony algorithms is that they take into account not only the links (i.e., passages) existing between vertices but also their absence during a long period. Thus, if a particular path (sequence) is not followed after some time, the pheromone left there will “evaporate”.
- Last POI in the current itinerary (input no. 7)The last POI in current version of the itinerary (under construction) is also used to evaluate the score of candidate POIs. In fact, the pheromone (sequence pertinence) can only be assessed when two POIs are being analysed (the last one in the sequence and the new one to be added).
- User preference pertinence ()We adopt the hypothesis that if the user is interested in a thematic category or historical period, then he is also interested in other semantically similar categories. For example, if the user likes visiting churches, he might enjoy visiting a monastery too. These are two semantically close concepts. In this sense, the function that calculates the pertinence of a POI according to the user’s preference actually measures the semantic similarity between categories that the user is interested in and categories of the POI.
- Temporal pertinence ()The temporal pertinence is defined according to the travel time () between the last POI added to the itinerary and the candidate POI . It also takes into consideration the remaining available time for the trip.Formula (3) formally defines the temporal pertinence of a POI.In this model, the shorter the time to reach the candidate POI, the greater its score (closer to 1).
- Social PertinenceSocial pertinence is defined by the number of users at instant t who recently went from the last POI added to the itinerary to the candidate POI. This pertinence also takes into account the popularity of the candidate POI at instant t of the evaluation. To define the social pertinence of a POI, we use the graph.Formula (4) defines the social pertinence of a POI formally.where is the last POI added to the itinerary, is the popularity of at instant t, and is represented by the frequency with which tourists travelled from to at instant t.The popularity of a POI is defined in terms of the frequency at which users travelled from the last POI to the POI being analysed and by the score given by other users evaluating the POI. The popularity takes into account the neighbourhood context: indeed, it is normalised by the score and the visit frequency of neighbouring POIs. Note that a set of neighbour POIs is determined by a POI and a geographic neighbourhood radius, which is defined as parameter.Thus, the popularity of a POI is defined by Formula (5):where V is a list of POIs, neighbours of , ranked by their overall assessment ratings. Thus, is a function that computes the position of within the list V according to the average of ratings associated with . In the same way, is a list of POIs, neighbours of , ranked in terms of the frequency with which they have been visited from their neighbouring POIs. The function is a function that computes the position of within the list .Thus, the closer the rank of a POI is to the first position, the higher is its popularity score.The intensity of a link between two POIs and is defined as follows:and are explained in detail below.According to the “ant colony” approach incorporated in our algorithm, a user is represented by an ant. For this reason, when a user visits a POI and then a POI , he deposits pheromones on the path between and (see Section 4.4.1, Figure 13). For each passage on the path - , a quantity Q (predefined) of pheromones is accumulated on the corresponding edge in the graph. corresponds to the total amount (quantity) of pheromones that is accumulated between and at instant t. Finally, corresponds to the maximal value of in the graph (, where each is the quantity of the accumulated pheromones on the paths between POIs of a given city. is dynamic and evolves over time.Thus, when the amount of pheromones on the path – is high (close to ) due to many users taking this path, a visitor should be invited to take the path – when he visits the POI or vice versa.Note that is stored in the graph. This total amount of pheromones is updated every time a user takes this path. Thus, the reinforcement principle of pheromones between and is defined byMoreover, the total amount of pheromone on a path (noted ) decreases progressively if nobody follows this path for a certain period of time.The evaporation principle of pheromones is defined by formula (8):where is the evaporation rate.
4.5.1. Experimentation of Our Itinerary Generation Approach
4.5.2. Comparison to Similar Approaches
Conflicts of Interest
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Rajaonarivo, L.; Fonteles, A.; Sallaberry, C.; Bessagnet, M.-N.; Roose, P.; Etcheverry, P.; Marquesuzaà, C.; Lacayrelle, A.L.P.; Cayèré, C.; Coudert, Q. Recommendation of Heterogeneous Cultural Heritage Objects for the Promotion of Tourism. ISPRS Int. J. Geo-Inf. 2019, 8, 230. https://doi.org/10.3390/ijgi8050230
Rajaonarivo L, Fonteles A, Sallaberry C, Bessagnet M-N, Roose P, Etcheverry P, Marquesuzaà C, Lacayrelle ALP, Cayèré C, Coudert Q. Recommendation of Heterogeneous Cultural Heritage Objects for the Promotion of Tourism. ISPRS International Journal of Geo-Information. 2019; 8(5):230. https://doi.org/10.3390/ijgi8050230Chicago/Turabian Style
Rajaonarivo, Landy, André Fonteles, Christian Sallaberry, Marie-Noëlle Bessagnet, Philippe Roose, Patrick Etcheverry, Christophe Marquesuzaà, Annig Le Parc Lacayrelle, Cécile Cayèré, and Quentin Coudert. 2019. "Recommendation of Heterogeneous Cultural Heritage Objects for the Promotion of Tourism" ISPRS International Journal of Geo-Information 8, no. 5: 230. https://doi.org/10.3390/ijgi8050230