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Estimation of Personal Preferences on Points of Interest

机译:对兴趣点的个人偏好估算

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摘要

The goal of our study is to recommend a travel plan that a tourist can change his/her state of mind to positive feeling. A personal preference of a point of interest (POI) differs individually. Moreover, the general impressions and the personal knowledge of a POI influence the personal preference of it. Therefore, it is difficult for a tourist to make a travel plan concerning POIs where a tourist has never visited, or he/she has not visited much. In this paper, we propose a method to estimate personal preferences on the unknown POIs by using the general impressions and the personal knowledge concerning known POIs. We estimate personal preferences concerning unknown POIs through shaping parameters about the impression and knowledge of POIs involved in personal preference about POIs. We have designed a hierarchical Bayesian model via Markov chain Monte Carlo technique for parameter estimation. As for 15 subjects, we have designed the model by using the general impressions and the personal knowledge concerning 84 POIs. They evaluated the validity of the results of which the model has estimated individual preferences for unknown POI. As for nine of 15 subjects, the result shows that the estimation accuracy is more than 60%. It was shown that the general impression and the personal knowledge of POIs affected the preference.
机译:我们研究的目标是推荐一个旅游计划,旅游计划可以将他/她的心态改变为积极的感觉。个人偏好感兴趣(POI)不同。此外,POI对POI的一般印象和个人知识影响了它的个人偏好。因此,旅游难以制定一个旅行计划,其中一个游客从未参观过游客,或者他/她没有访问过多。在本文中,我们提出了一种通过使用普遍印象和有关已知毒品的个人知识来估算未知毒性的个人偏好的方法。我们通过塑造关于涉及POI的POIS的印象和知识的参数来估算有关未知POI的个人偏好。我们设计了一款通过Markov Chain Monte Carlo技术的分层贝叶斯模型,用于参数估计。至于15个科目,我们通过使用普通印象和有关84 POI的个人知识设计了该模型。他们评估了该模型估计未知POI的个体偏好的结果的有效性。至于15个受试者的九个,结果表明估计精度超过60 %。结果表明,POI的一般印象和个人知识影响了偏好。

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