Context Based- Tourism Recommender System: Towards Tourists' Context-Sensitive Preference Conceptual Model

Achmad, Kusuma Adi; Nugroho, Lukito Edi; Diunaedi, Achmad; Widyawan,

doi:10.1109/icstc.2018.8528676

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…K-Means was applied by keeping all the parameters except n_clusters to their default values. In order to identify the optimal number of clusters, various values were tested in the range [2][3][4][5][6][7][8][9][10][11][12][13][14][15] by calculating the corresponding silhouette coefficients. Besides K-Means, Affinity propagation and Mean-shift clustering algorithms were also tested but with reached lower silhouette coefficients.…”

Section: B Descriptive Analysis Resultsmentioning

confidence: 99%

“…Prescriptive Analytics: AI methods (Machine Learning and optimization) to provide recommendations, by automatically solving decision models. In this context, the project [5] proposes, referring to the tourist domain, a context-aware based restaurant recommender system. Similarly the LUME-PLANNER project [6] integrates multiple AI-methods to recommend destination and routes to tourists in a personalized way.…”

Section: Related Workmentioning

confidence: 99%

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Towards Smart Cities for Tourism: the POLIS-EYE Project

Seravalli

Busani

Venturi

et al. 2022

2022 IEEE International Smart Cities Conference (ISC2)

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Novel and widespread ICT and Internet of Things (IoT) technology can provide fine-grained real-time information to the tourist sector, both to support the demand side (tourists) and the supply side (managers and organizers). We present the POLIS-EYE project that aims to build decision-support systems helping tourist-managers to organize and optimize policies and resources. In particular, we focus on a service to monitor and forecast people presence in tourist areas by combining heterogeneous datasets with a special focus on data collected from the mobile phone network.

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Section: B Descriptive Analysis Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Towards Smart Cities for Tourism: the POLIS-EYE Project

Seravalli

Busani

Venturi

et al. 2022

2022 IEEE International Smart Cities Conference (ISC2)

View full text Add to dashboard Cite

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“…Deep learning is used for several common areas where the most focused domain among research is computer vision, prediction, semantic analysis, natural language processing, information retrieval, and customer relationship management is one of the newer areas of application of deep learning (Ho et al, 2021). Deep learning has shown high accuracy in other applications such as context-based recommender system (Achmad et al, 2019) for restaurants (see…”

Section: Deep Learning Applicationsmentioning

confidence: 99%

Predicting Consumption Intention of Consumer Relationship Management Users Using Deep Learning Techniques: A Review

Alaros¹,

Marjani²,

Shafiq³

et al. 2022

Indonesian J. Sci. Technol

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Consumer/customer relationship management (CRM) can potentially influence business as it predicts changes in people’s perspectives, which could impact future sales. Accordingly, advancements in Information Technology are under investigation to see their capabilities to improve the work of CRM. Many prediction techniques, such as Data Mining, Machine Learning (ML), and Deep Learning (DL), were found to be utilized with CRM. ML methods were found to dominate other approaches in terms of the prediction of consumers’ intention to purchase. This review provides DL algorithms that are mostly used in the last five years, to support CRM to predict purchase intention for better product sales decisions. Prediction criteria related to online activities and behavior were found to be the most inputs of prediction models. DL approaches are slowly applied within purchase intention prediction due to their advanced capabilities in handling large and complicated datasets with minimum human supervision. DL models such as CNN and LSTM result in high accuracy in prediction intention with 98%. Future research uses the two algorithms (CNN, LSTM) compiled to make the best prediction consumption in CRM. Additionally, an effort is being made to create a framework for predicting purchases based on many DL algorithms and the most pertinent characteristics.

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