Although abundant research work has been published in the area of path recommendation and its applications on travel and routing topics, scarce work has been reported on context-aware route recommendation systems aimed to stimulate optimal cultural heritage experiences. This paper tries to address this issue, by proposing a personalized and content adaptive cultural heritage path recommendation system, where location is modeled using mean-shift clustering trained with actual user movement patters. Additionally, topic modeling is incorporated to formalize the implicit cultural heritage content, while first order Markov models address the movement as a temporal transition aspect of the problem. The overall architecture is applied on data collected from actual visits to the archaeological sites of Gournia and Çatalhöyük and extensive analysis on visitor movement patterns follows, especially in comparison to the curated paths in the aforementioned sites. Finally, the offline evaluation results of the proposed recommendation scheme are encouraging, validating its efficiency and setting a positive paradigm for cultural heritage route recommendations.

中文翻译：

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个性化和内容自适应的文化遗产路径推荐：对 Gournia 和 Çatalhöyük 考古遗址的应用

尽管在路径推荐及其在旅行和路线主题上的应用方面已经发表了大量研究工作，但关于旨在激发最佳文化遗产体验的上下文感知路线推荐系统的报道很少。本文试图通过提出个性化和内容自适应的文化遗产路径推荐系统来解决这个问题，该系统使用经过实际用户移动模式训练的均值漂移聚类对位置进行建模。此外，主题建模被纳入以形式化隐含的文化遗产内容，而一阶马尔可夫模型将运动作为问题的时间转换方面来解决。整体架构应用于从对 Gournia 和 Çatalhöyük 考古遗址的实际访问中收集的数据，并对游客移动模式进行广泛分析，特别是与上述遗址中的策划路径进行比较。最后，所提出的推荐方案的线下评估结果令人鼓舞，验证了其效率，并为文化遗产路线推荐树立了积极的范式。