Contextual Recommender Systems for Building and Construction Materials Business

วิศวกรรมศาสตรมหาบัณฑิต (วิศวกรรมคอมพิวเตอร์), 2565Nowadays, the recommendation system is one of the most important supported technologies to e-commerce that aims for recommending the products or services to be purchased, to increase sales. In this work, the focus on the recommendation system for the building materials business. Building materials business is a business that sales construction related materials and equipment, such as, structural goods, tools supplies, etc. For customers who come to buy products will builder professionally or customers who want to improve their homes. Products recommendation system in this business will recommend products that can be used in profession. Generally, system recommends products that are like the ones purchased but regardless of context or profession of the customer. In this paper, we propose a context awareness data modeling to specialize the recommendation system aiming for the building materials business.ทุนโครงการการอุดมศึกษาเพื่ออุตสาหกรรม (Higher Education for Industry: Hi-FI)ปัจจุบันระบบการแนะนำเป็นหนึ่งในเทคโนโลยีสำคัญที่สนับสนุนสำหรับอีคอมเมิร์ซ โดยมีจุดมุ่งหมายเพื่อแนะนำสินค้าหรือบริการที่ตรงตามความต้องการของผู้ซื้อ เพื่อเพิ่มยอดขายสำหรับธุรกิจ ในการวิจัยนี้เรามุ่งเน้นไปที่การพัฒนาระบบแนะนำสำหรับธุรกิจวัสดุก่อสร้าง ธุรกิจวัสดุก่อสร้างเป็นธุรกิจที่จำหน่ายวัสดุก่อสร้างและอุปกรณ์ที่เกี่ยวข้อง เช่น สินค้าโครงสร้าง อุปกรณ์เครื่องมือ และอื่นๆ สำหรับลูกค้าที่มาซื้อผลิตภัณฑ์จะเป็นช่างก่อสร้างหรือลูกค้าที่ต้องการปรับปรุงบ้าน ระบบแนะนำสินค้าในธุรกิจนี้จะแนะนำสินค้าที่สามารถนำไปใช้ในอาชีพได้ โดยทั่วไป ระบบจะแนะนำผลิตภัณฑ์ที่คล้ายกับที่ซื้อ แต่ไม่คำนึงถึงบริบทหรืออาชีพของลูกค้า โดยในงานวิจัยนี้ เราได้นำเสนอการสร้างแบบจำลองข้อมูลการรับรู้บริบท เพื่อที่จะพัฒนาระบบแนะนำสำหรับธุรกิจวัสดุก่อสร้า

Quantifying usability prioritization using k-means clustering algorithm on hybrid metric features for MAR learning

This paper presents and discusses an empirical work of using machine learning K-means clustering algorithm in analyzing and processing Mobile Augmented Reality (MAR) learning usability data. This paper first discusses the issues within usability and machine learning spectrum, then explain in detail a proposed methodology approaching the experiments conducted in this research. This contributes in providing empirical evidence on the feasibility of K-means algorithm through the discreet display of preliminary outcomes and performance results. This paper also proposes a new usability prioritization technique that can be quantified objectively through the calculation of negative differences between cluster centroids. Towards the end, this paper will discourse important research insights, impartial discussions and future works

Usability framework for mobile augmented reality language learning

After several decades since its introduction, the existing ISO9241-11 usability framework is still vastly used in Mobile Augmented Reality (MAR) language learning. The existing framework is generic and can be applied to diverse emerging technologies such as electronic and mobile learning. However, technologies like MAR have interaction properties that are significantly unique and require different usability processes. Hence, implementing the existing framework on MAR can lead to non-optimized, inefficient, and ineffective outcomes. Furthermore, state-of-the-art analysis models such as machine learning are not apparent in MAR usability studies, despite evidence of positive outcomes in other learning technologies. In recent MAR learning studies, machine learning benefits such as problem identification and prioritization were non-existent. These setbacks could slow down the advancement of MAR language learning, which mainly aims to improve language proficiency among MAR users, especially in English communication. Therefore, this research proposed the Usability Framework for MAR (UFMAR) that addressed the currently identified research problems and gaps in language learning. UFMAR introduced an improved data collection method called Individual Interaction Clustering-based Usability Measuring Instrument (IICUMI), followed by a machine learning-driven analysis model called Clustering-based Usability Prioritization Analysis (CUPA) and a prioritization quantifier called Usability Clustering Prioritization Model (UCPM). UFMAR showed empirical evidence of significantly improving usability in MAR, capitalizing on its unique interaction properties. UFMAR enhanced the existing framework with new abilities to systematically identify and prioritize MAR usability issues. Through the experimental results of UFMAR, it was found that the IICUMI method was 50% more effective, while CUPA and UCPM were 57% more effective than the existing framework. The outcome through UFMAR also produced 86% accuracy in analysis results and was 79% more efficient in framework implementation. UFMAR was validated through three cycles of the experimental processes, with triangulation through expert reviews, to be proven as a fitting framework for MAR language learning