Abstract
Recent developments in digital technologies regarding the cultural heritage domain have driven technological trends in comfortable and convenient traveling, by offering interactive and personalized user experiences. The emergence of big data analytics, recommendation systems and personalization techniques have created a smart research field, augmenting cultural heritage visitor’s experience. In this work, a novel, hybrid recommender system for cultural places is proposed, that combines user preference with cultural tourist typologies. Starting with the McKercher typology as a user classification research base, which extracts five categories of heritage tourists out of two variables (cultural centrality and depth of user experience) and using a questionnaire, an enriched cultural tourist typology is developed, where three additional variables governing cultural visitor types are also proposed (frequency of visits, visiting knowledge and duration of the visit). The extracted categories per user are fused in a robust collaborative filtering, matrix factorization-based recommendation algorithm as extra user features. The obtained results on reference data collected from eight cities exhibit an improvement in system performance, thereby indicating the robustness of the presented approach.
Highlights
In recent years, user adaptive systems have become popular in many application areas, including the cultural tourism domain, which is nowadays recognized as one of the most important forms of touristic traffic
In order to study the effect of the computed user personas, the second recommender systems (RS) is based on LightFM, but the factorization scheme is hybrid in this case; the user features are augmented with the five additional personas features computed in Section 4.2, designating the extend to which each user belongs to each of the 5 pre-defined cultural tourist type categories
The test set size has been set to 20% of the total number of interactions and the results presented in Figures 5 and 6 are the averages of 10 different runs, whose statistical significance is assessed by the Wilcoxon singed rank test
Summary
User adaptive systems have become popular in many application areas, including the cultural tourism domain, which is nowadays recognized as one of the most important forms of touristic traffic. The large number of resources and data available online have resulted in the fast dissemination of cultural information, but, on the other hand, they have contributed to the problem of information overload; that is, the difficulty in identifying those resources best suited to each individual’s needs. Managing these voluminous resources with principles and techniques pertaining to big data analytics, in an effort to offer suitable and personalized support to visitors, constitutes one the most interesting challenges in this research field. Cultural tourism shows remarkable growth and popularity, little research has been done to categorize cultural tourists by integrating both their cultural centrality (e.g., cultural motivation, importance of culture in the decision to visit) and depth/levels of cultural experience [2,3,4]
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