Design aesthetics recommender system based on customer profile and wanted affect

Product recommendation systems have been instrumental in online commerce since the early days. Their development is expanded further with the help of big data and advanced deep learning methods, where consumer profiling is central. The interest of the consumer can now be predicted based on the personal past choices and the choices of similar consumers. However, what is currently defined as a choice is based on quantifiable data, like the product features, cost, and type. This paper investigates the possibility of profiling customers based on the preferred product design and wanted affects. We considered the case of vase design, where we study individual Kansei of each design. The personal aspects of the consumer considered in this study were decided based on our literature review conclusions on the consumer response to product design. We build a representative consumer model that constitutes the recommendation system's core using deep learning. It asks the new consumers to provide what affect they are looking for, through Kansei adjectives, and recommend; as a result, the aesthetic design that will most likely cause that affect

Design aesthetics recommender system based on customer profile and wanted affect

Product recommendation systems have been instrumental in online commerce since the early days. Their development is expanded further with the help of big data and advanced deep learning methods, where consumer profiling is central. The interest of the consumer can now be predicted based on the personal past choices and the choices of similar consumers. However, what is currently defined as a choice is based on quantifiable data, like product features, cost, and type. This paper investigates the possibility of profiling customers based on the preferred product design and wanted affects. We considered the case of vase design, where we study individual Kansei of each design. The personal aspects of the consumer considered in this study were decided based on our literature review conclusions on the consumer response to product design. We build a representative consumer model that constitutes the recommendation system's core using deep learning. It asks the new consumers to provide what affect they are looking for, through Kansei adjectives, and recommend; as a result, the aesthetic design that will most likely cause that affect.Comment: 11 pages, 10 figures, peer-reviewed at the KEER 2022 conferenc

User-Oriented Preference Toward a Recommender System

            في الوقت الحاضر، من الملائم لنا استخدام محرك بحث للحصول على المعلومات المطلوبة. لكن في بعض الأحيان يسيء فهم المعلومات بسبب التقارير الإعلامية المختلفة. نظام التوصية (RS) شائع الاستخدام في كل الأعمال لأنه يمكن أن يوفر معلومات للمستخدمين التي ستجذب المزيد من الإيرادات للشركات. ولكن أيضًا ، في بعض الأحيان ، يوصي النظام المستخدمين بالمعلومات غير الضرورية. لهذا السبب ، قدم هذا البحث بنية لنظام التوصية التي يمكن أن تستند إلى التفضيل الموجه للمستخدم. هذا النظام يسمى UOP-RS. لجعل UOP-RS بشكل كبير، ركزهذا البحث على معلومات السينما وتجميع قاعدة بيانات الأفلام من موقع IMDb الذي يوفر معلومات متعلقة بالأفلام والبرامج التلفزيونية ومقاطع الفيديو المنزلية وألعاب الفيديو والمحتوى المتدفق الذي يجمع أيضًا العديد من التقييمات والمراجعات من المستخدمين. حلل البحث أيضًا بيانات المستخدم الفردي لاستخراج ميزات المستخدم. بناءً على خصائص المستخدم ، وتقييمات / درجات الفيلم ، ونتائج الأفلام ، تم بناء نموذج UOP-RS. في تجربتنا ، تم استخدام 5000 مجموعة بيانات أفلام IMDb و 5 أفلام موصى بها للمستخدمين. تظهر النتائج أن النظام يمكنه إرجاع النتائج في 3.86 ثانية ولديه خطأ 14٪ على السلع الموصى بها عند تدريب البيانات على أنها K = 50. في نهاية هذه الورقة خلص إلى أن النظام يمكن أن يوصي بسرعة مستخدمي السلع التي يحتاجون إليها. سوف يمتد النظام المقترح للاتصال بنظام Chatbot بحيث يمكن للمستخدمين جعل الاستعلامات أسرع وأسهل من هواتفهم في المستقبل.            Nowadays, it is convenient for us to use a search engine to get our needed information. But sometimes it will misunderstand the information because of the different media reports. The Recommender System (RS) is popular to use for every business since it can provide information for users that will attract more revenues for companies. But also, sometimes the system will recommend unneeded information for users. Because of this, this paper provided an architecture of a recommender system that could base on user-oriented preference. This system is called UOP-RS. To make the UOP-RS significantly, this paper focused on movie theatre information and collect the movie database from the IMDb website that provides information related to movies, television programs, home videos, video games, and streaming content that also collects many ratings and reviews from users. This paper also analyzed individual user data to extract the user’s features. Based on user characteristics, movie ratings/scores, and movie results, a UOP-RS model was built. In our experiment, 5000 IMDb movie datasets were used and 5 recommended movies for users. The results show that the system could return results on 3.86 s and has a 14% error on recommended goods when training data as . At the end of this paper concluded that the system could quickly recommend users of the goods which they needed.  The proposed system will extend to connect with the Chatbot system that users can make queries faster and easier from their phones in the future

Semantic valence modeling: emotion recognition and affective states in context-aware systems

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Defining and describing a context requires knowledge (contextual information), while research is addressing a wider range of potential contextual information in a diverse range of domains the diversity of potential contextual information has not been effectively addressed. This paper considers the implementation of context and identifies emotion (more accurately emotional response) as a factor in the personalization of services as under-represented in the literature. We propose semantic valence modeling implemented in fuzzy rule-based systems as a potential solution to the implementation of emotional responses in context-aware systems. It is concluded that the effective implementation of emotional responses based on the posited approach will improve the accuracy of personalized service provision and additionally offers the potential to improve the levels of computational intelligence in context-aware domains and systems.Peer ReviewedPostprint (author's final draft

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202

Use of machine learning techniques in the Kansei engineering synthesis phase

Una de las principales metodologías para el diseño emocional de productos es la ingeniería Kansei. En esta técnica se pretende relacionar las propiedades del producto o servicio con las sensaciones percibidas por los usuarios. Una aplicación clásica de esta metodología requiere distintas fases entre la que se encuentran la elección del dominio del diseño, la definición del espacio semántico y de propiedades, la síntesis, la validación y la construcción del modelo. La popularización de las técnicas de inteligencia artificial, entre las que se encuentra el aprendizaje automático, ha llevado a muchos autores a utilizar estas herramientas en la fase de síntesis. En este trabajo se analizan las principales herramientas de aprendizaje automático usadas en la fase de síntesis de ingeniería kansei, así como la adecuación de su uso, en base al espacio de propiedades previamente definido.Kansei engineering is one of the main methodologies for the emotional design of products. This technique aims to relate the properties of the product or service to the sensations perceived by users. A classic application of this methodology requires different phases, among which are the choice of the product domain, the definition of the semantic space and properties, the elaboration of the synthesis, the validation and the construction of the model and validation. The popularization of artificial intelligence techniques, including machine learning, has led many authors to use these mathematical models in the synthesis phase. This paper analyses the main machine learning tools used in the synthesis phase of kansei engineering, as well as the relevance of their use, based on the property space previously described

Decision Support System for Inventory Control of Raw Material

PT Suwarni Agro Mandiri Plant Pariaman is a company which produces fertilizer. This company has a problem related to raw material inventory. The inventory can be overstock or stock out. It is due to their working which is not guided by an information system. Therefore, this research proposes a decision support system for controlling the inventory of the raw material. The system uses Material Requirement Planning (MRP) approach and is designed in three sub-systems. They are OLTP database for managing the daily activities, MRP for determining the lot size and the raw material ordering time, and OLAP with data warehouse for analyzing the raw material data. Keywords-inventory; inventory control; online analytical processing; online transaction processin

Human Being Emotion in Cognitive Intelligent Robotic Control Pt I: Quantum / Soft Computing Approach

Abstract. The article consists of two parts. Part I shows the possibility of quantum / soft computing optimizers of knowledge bases (QSCOptKB™) as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface. In particular, case, the aim of this part is to demonstrate the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKB™ and QCOptKB™ sophisticated toolkit. Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described. The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown. Developed information technology examined with special (difficult in diagnostic practice) examples emotion state estimation of autism children (ASD) and dementia and background of the knowledge bases design for intelligent robot of service use is it. Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.