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dc.contributor.authorElKafrawy, Passent
dc.contributor.authorBennawy, Mohamed
dc.date.accessioned2023-03-16T05:13:53Z
dc.date.available2023-03-16T05:13:53Z
dc.date.issued2022-07-02
dc.identifier.citationBennawy, M., el-Kafrawy, P. (2022). Recommendations on Streaming Data: E-Tourism Event Stream Processing Recommender System. In: Kahraman, C., Tolga, A.C., Cevik Onar, S., Cebi, S., Oztaysi, B., Sari, I.U. (eds) Intelligent and Fuzzy Systems. INFUS 2022. Lecture Notes in Networks and Systems, vol 505. Springer, Cham. https://doi.org/10.1007/978-3-031-09176-6_59en_US
dc.identifier.doihttps://doi.org/10.1007/978-3-031-09176-6_59en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/691
dc.description.abstractThe Association for Computing Machinery ACM recommendation systems challenge (ACM RecSys) [1] released an e-tourism dataset for the first time in 2019. Challenge shared hotel booking sessions from trivago website asking to rank the hotels list for the users. Better ranking should achieve higher click out rate. In this context, Trivago dataset is very important for e-tourism recommendation systems domain research and industry as well. In this paper, description for dataset characteristics and proposal for a session-based recommender system in addition to a comparison of several baseline algorithms trained on the data. The developed model is personalized session-based recommender taking into consideration user search preferences. Technically, paper compare between six different models vary from learning to rank, nearest neighbor and popularity approaches and compared results with two benchmark accuracy. Taking into consideration the ability to deploy model into production environments and the accuracy evaluation based on mean reciprocal rate as per challenge guidelines. Our winning experiment is using one learning to rank model achieving 0.64 mean reciprocal rate compared to 37 model achieving 0.68 by ACM challenge winning team [2].en_US
dc.publisherSpringer International Publishingen_US
dc.subjectEvent stream processingen_US
dc.subjectKafkaen_US
dc.subjectSparken_US
dc.subjectMachine learningen_US
dc.subjectSession-Based recommender systemsen_US
dc.titleRecommendations on Streaming Data: E-Tourism Event Stream Processing Recommender Systemen_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labArtificial Intelligence & Cyber Security Laben_US
dc.subject.KSAICTen_US
dc.source.indexScopusen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.firstauthorBennawy, Mohamed
dc.conference.nameIntelligent and Fuzzy Systems Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conferenceen_US
dc.conference.date2022


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