Sem-Fit: A semantic based expert system to provide recommendations in the tourism domain

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• 作者：Á ; ngel Garc&#xed ; a-Crespo¹ ; ^a ; ^{angel.garcia@uc3m.es"" rel=""nofollow} ; José ; Luis Ló ; pez-Cuadrado² ; ^a ; ^{joseluis.lopez.cuadrado@uc3m.es"" rel=""nofollow} ; Ricardo Colomo-Palacios ; ^a ; ^{ricardo.colomo@uc3m.es"" rel=""nofollow} ; Israel Gonzá ; lez-Carrasco² ; ^a ; ^{Israel.gonzalez@uc3m.es"" rel=""nofollow} ; Belé ; n Ruiz-Mezcua³ ; ^a ; ^{belen.ruiz@uc3m.es"" rel=""nofollow}
• 关键词：Semantic technologies ; Semantic labeling ; Fuzzy logic ; Recommender systems ; Hotels
• 刊名：Expert Systems with Applications
• 出版年：2011
• 出版时间：15 September 2011
• 年：2011
• 卷：38
• 期：10
• 页码：13310-13319
• 全文大小：589 K

文摘

The hotel industry is one of the leading stakeholders in the tourism sector. In order to reduce the traveler’s cost of seeking accommodations, enforce the return ratio efficiency of guest rooms and enhance total operating performance, evaluating and selecting a suitable hotel location has become one of the most critical issues for the hotel industry. In this scenario, recommender services are increasingly emerging which employ intelligent agents and artificial intelligence to “cut through” unlimited information and obtain personalized solutions. Taking this assumption into account, this paper presents Sem-Fit, a semantic hotel recommendation expert system, based on the consumer’s experience about recommendation provided by the system. Sem-Fit uses the consumer’s experience point of view in order to apply fuzzy logic techniques to relating customer and hotel characteristics, represented by means of domain ontologies and affect grids. After receiving a recommendation, the customer provides a valuation about the recommendation generated by the system. Based on these valuations, the rules of the system are updated in order to adjust the new recommendations to past user experiences. To test the validity of Sem-Fit, the validation accomplished includes the interaction of the customer with the system and then the results are compared with the expert recommendation for each customer profile. Moreover, the values of precision and recall and F1 have been calculated, based on three points of view, to measure the degree of relevance of the recommendations of the fuzzy system, showing that the system recommendations are on the same level as an expert in the domain.