Incorporating Time Constraints into a Recommender System for Museum Visitors

La-or Kovavisaruch; Taweesak Sanpechuda; Krisada Chinda; Thitipong Wongsatho; Sodsai Wisadsud; Anuwat Chaiwongyen

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자료유형: 학술저널

저자정보: La-or Kovavisaruch (National Electronics and Computer Technology Center) Taweesak Sanpechuda (National Electronics and Computer Technology Center) Krisada Chinda (National Electronics and Computer Technology Center) Thitipong Wongsatho (National Electronics and Computer Technology Center) Sodsai Wisadsud (National Electronics and Computer Technology Center) Anuwat Chaiwongyen (National Electronics and Computer Technology Center)

저널정보: 한국정보통신학회JICCE Journal of information and communication convergence engineering Journal of information and communication convergence engineering Vol.18 No.2

발행연도: 2020.6

수록면: 123 - 131 (9page)

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After observing that most tourists plan to complete their visits to multiple cultural heritage sites within one day, we surmised that for many museum visitors, the foremost thought is with regard to the amount of time is to be spent at each location and how they can maximize their enjoyment at a site while still balancing their travel itinerary? Recommendation systems in e-commerce are built on knowledge about the users’ previous purchasing history; recommendation systems for museums, on the other hand, do not have an equivalent data source available. Recent solutions have incorporated advanced technologies such as algorithms that rely on social filtering, which builds recommendations from the nearest identified similar user. Our paper proposes a different approach, and involves providing dynamic recommendations that deploy social filtering as well as content-based filtering using term frequency-inverse document frequency. The main challenge is to overcome a cold start, whereby no information is available on new users entering the system, and thus there is no strong background information for generating the recommendation. In these cases, our solution deploys statistical methods to create a recommendation, which can then be used to gather data for future iterations. We are currently running a pilot test at Chao Samphraya national museum and have received positive feedback to date on the implementation.

#Mobile application #Museum guide #Recommendation system

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