Singapore
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About

Life-long learner, researcher and developer who passionate in developing intelligent…

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Experience & Education

  • Amazon Web Services (AWS)

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Publications

  • Characterizing Bots and Humans in Social Media

    International Conference on Computational Social Science (IC2S2)

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  • Socio-physical analytics: challenges & opportunities

    ACM

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  • Collective churn prediction in social networks

    Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

    Abstract—In service-based industries, churn poses a significant threat to the integrity of the user communities and profitability of the service providers. As such, research on churn prediction methods has been actively pursued, involving either intrinsic, user profile factors or extrinsic, social factors. However, existing approaches often address each type of factors separately, thus lacking a comprehensive view of churn behaviors. In this paper, we propose a new churn prediction approach…

    Abstract—In service-based industries, churn poses a significant threat to the integrity of the user communities and profitability of the service providers. As such, research on churn prediction methods has been actively pursued, involving either intrinsic, user profile factors or extrinsic, social factors. However, existing approaches often address each type of factors separately, thus lacking a comprehensive view of churn behaviors. In this paper, we propose a new churn prediction approach based on collective classification (CC), which accounts for both the intrinsic and extrinsic factors by utilizing the local features of, and dependencies among, individuals during prediction steps. We evaluate our CC approach using real data provided by an established mobile social networking site, with a primary focus on prediction of churn in chat activities. Our results demonstrate that using CC and social features derived from interaction records and network structure yields substantially improved prediction in comparison to using conventional classification and user profile features only.

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  • A Business Zone Recommender System Based on Facebook and Urban Planning Data

    Proceedings of the 38th European Conference on IR Research (ECIR'16)

    If you were to open your own cafe shop, would you not want to know the best location to set it up at? To answer this, we present ZoneRec—a zone recommendation system for businesses in an urban city, which combines public business data from Facebook and urban planning data. The core of our system consists of machine learning algorithms that take in a business’ textual/categorical description and output a list of recommended zones to establish the business at. We have evaluated our system using…

    If you were to open your own cafe shop, would you not want to know the best location to set it up at? To answer this, we present ZoneRec—a zone recommendation system for businesses in an urban city, which combines public business data from Facebook and urban planning data. The core of our system consists of machine learning algorithms that take in a business’ textual/categorical description and output a list of recommended zones to establish the business at. We have evaluated our system using food businesses data from Singapore, and assessed the contribution of different features to the recommendation quality.

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Projects

  • Socio-Physical Business Analytics

    - Present

    Data:
    Facebook, Twitter, Instagram, Google Maps, Open Street Maps

    Technologies Used:
    Python, scikit-learn, Node.js, MongoDB, Elasticsearch, RabbitMQ

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Languages

  • English

    Full professional proficiency

  • Indonesian

    Native or bilingual proficiency

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