The paper titled "Design aesthetics recommender system based on customer profile and wanted affect" explores the potential of profiling customers based on their preferred product design and desired effects. The authors highlight the importance of product recommendation systems in online commerce, which have been further enhanced by big data and advanced deep learning techniques. These systems rely on consumer profiling to predict consumer interests based on their past choices and the choices of similar consumers. However, the current definition of choice is limited to quantifiable data such as product features, cost, and type. To address this limitation, the study focuses on profiling customers based on their preferences for product design and desired effects. Specifically, the authors examine individual Kansei (aesthetic) aspects of vase designs to understand how they influence consumer responses. To establish a comprehensive understanding of consumer preferences, the researchers conduct a literature review that informs their selection of personal aspects considered in the study. They then develop a representative consumer model using deep learning techniques as the core of their recommendation system. The proposed system prompts new consumers to specify the desired effect they are looking for using Kansei adjectives. Based on this input, the system recommends an aesthetic design that is most likely to evoke that particular effect. In conclusion, this paper contributes to expanding existing product recommendation systems by incorporating customer profiles based on preferred product design and desired affects. By leveraging deep learning methods and Kansei adjectives, the proposed system aims to enhance user satisfaction by recommending aesthetic designs that align with individual preferences and desired effects.
Profiling customers means studying and understanding what kind of products they like and how those products make them feel. Product recommendation systems are important because they help people find things they might like to buy when shopping online. These systems can be made even better by using big data (lots of information) and advanced deep learning techniques (smart computer programs). Right now, there is a problem with how we define choices for customers based on their preferences. The focus is on studying what kinds of designs and effects people like in vases, and how those designs affect their feelings. Researchers are looking at different studies to decide which personal aspects to include in their study. They want to create a computer program that can recommend the best vase design for each person based on what effect they want it to have. This will make existing product recommendation systems better by considering what customers like in terms of design and effects, so that people will be happier with their purchases."
Definitions
- Profiling: Studying and understanding someone's preferences or characteristics.
- Product recommendation systems: Computer programs that suggest things for people to buy based on their interests.
- Big data: A large amount of information.
- Advanced deep learning techniques: Smart computer programs that learn from lots of information.
- Preferences: What someone likes or prefers over something else.
- Desired effects: The specific results or feelings someone wants from a product.
- Kansei: Aesthetic aspects related to emotions or feelings.
- Literature review: Looking at different studies or research papers to gather
Design Aesthetics Recommender System Based on Customer Profile and Wanted Affect
Online commerce has been revolutionized by product recommendation systems, which have been further enhanced by big data and advanced deep learning techniques. These systems rely on consumer profiling to predict consumer interests based on their past choices and the choices of similar consumers. However, the current definition of choice is limited to quantifiable data such as product features, cost, and type. To address this limitation, a research paper titled “Design aesthetics recommender system based on customer profile and wanted affect” explores the potential of profiling customers based on their preferred product design and desired effects.
Background
The authors highlight the importance of understanding individual Kansei (aesthetic) aspects of vase designs in order to understand how they influence consumer responses. Kansei adjectives are used to describe an individual’s emotional response to certain products or experiences. The researchers conduct a literature review that informs their selection of personal aspects considered in the study.
Proposed System
The proposed system leverages deep learning methods combined with Kansei adjectives in order to develop a representative consumer model as the core of its recommendation system. This model prompts new consumers to specify the desired effect they are looking for using Kansei adjectives such as “elegant” or “dynamic” before recommending an aesthetic design that is most likely to evoke that particular effect.
Conclusion
In conclusion, this paper contributes to expanding existing product recommendation systems by incorporating customer profiles based on preferred product design and desired affects. By leveraging deep learning methods and Kansei adjectives, the proposed system aims to enhance user satisfaction by recommending aesthetic designs that align with individual preferences and desired effects.
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.
