This page has only limited features, please log in for full access.
Housing preference is the subjective and relative preference of users toward housing alternatives and studies in the field have been conducted to analyze the housing preferences of groups with sharing the same socio-demographic attributes. However, previous studies may not suggest the preference of individuals. In this regard, this study proposes “SeoulHouse2Vec,” an embedding-based collaborative filtering housing recommendation system for analyzing atypical and nonlinear housing preference of individuals. The model maps users and items in each dense vector space which are called embedding layers. This model may reflect trade-offs between the alternatives and recommend unexpected housing items and thus improve rational housing decision-making. The model expanded the search scope of housing alternatives to the entire city of Seoul utilizing public big data and GIS data. The preferences derived from the results can be used by suppliers, individual investors, and policymakers. Especially for architects, the architectural planning and design process will reflect users’ perspective and preferences, and provide quantitative data in the housing decision-making process for urban planning and administrative units.
Han Jun; Jae Kim; Deuk Rhee; Sun Chang. “SeoulHouse2Vec”: An Embedding-Based Collaborative Filtering Housing Recommender System for Analyzing Housing Preference. Sustainability 2020, 12, 6964 .
AMA StyleHan Jun, Jae Kim, Deuk Rhee, Sun Chang. “SeoulHouse2Vec”: An Embedding-Based Collaborative Filtering Housing Recommender System for Analyzing Housing Preference. Sustainability. 2020; 12 (17):6964.
Chicago/Turabian StyleHan Jun; Jae Kim; Deuk Rhee; Sun Chang. 2020. "“SeoulHouse2Vec”: An Embedding-Based Collaborative Filtering Housing Recommender System for Analyzing Housing Preference." Sustainability 12, no. 17: 6964.
In this study, we propose an electroencephalogram (EEG)-based long short-term memory networks model for recognizing user preferences toward architectural design images. An EEG is an approach that records the electrical activity in the brain, and EEG-based affection recognition is a technique used for quantitatively recognizing human emotion by analysing the recorded signals. Decision-makers’ subjective reactions toward architectural design alternatives may play a key role in the architectural planning and design stage. In this regard, the proposed model enables the quantitative recognition of their preferences and supports architects in the planning and design stages. The suggested model classifies the recorded data using a deep-learning technique. To build the model, an EEG recording experiment was conducted with 18 subjects, who were asked to select their most/least preferred images among eight images of small-housing design. Post recording, a positive and negative affect schedule questionnaire was distributed to the subjects to rate their affection. Google TensorFlow and Keras were used to structure the model. After training, precision, recall, and f1 score metrics were used to evaluate and validate the model. This model can help designers to evaluate design alternatives in terms of decision-making. Moreover, as this model uses biosignal data, which is universal to humans, architectural design processes for children, the elderly, etc., may be supported. Furthermore, a data-driven design database may be proposed in a future research for cross-validating with previous methods such as interviews and observations.
Sunwoo Chang; WonHyeok Dong; Hanjong Jun. Use of electroencephalogram and long short-term memory networks to recognize design preferences of users toward architectural design alternatives☆. Journal of Computational Design and Engineering 2020, 1 .
AMA StyleSunwoo Chang, WonHyeok Dong, Hanjong Jun. Use of electroencephalogram and long short-term memory networks to recognize design preferences of users toward architectural design alternatives☆. Journal of Computational Design and Engineering. 2020; ():1.
Chicago/Turabian StyleSunwoo Chang; WonHyeok Dong; Hanjong Jun. 2020. "Use of electroencephalogram and long short-term memory networks to recognize design preferences of users toward architectural design alternatives☆." Journal of Computational Design and Engineering , no. : 1.
Recent developments in real estate brokerage platforms have enabled residents to provide subjective reviews, which have immense value as subjective assessments and suggestions for architects. This study suggests a deep-learning-based natural language sentiment classification model to analyse reviews. Morpheme analysis and word embedding for ‘KoNLPy’ and ‘Word2vec’ were structured for pre-processing, and a long short-term memory network was used to process review data. Total 5974 review data were used in this study. Among the various active online platforms for real estate brokerage, platforms that provide online users with the ability to write reviews of their living spaces were crawled. The review data were classified as ‘positive’ or ‘negative’ by label and as ‘Apartment’ or ‘Non-Apartment’ by housing type. The model developed in this study is expected to increase in value as more online platforms appear in the future and the volume of natural language data generated by those platforms increases.
Sunwoo Chang; Won-Hyeok Dong; Deuk-Young Rhee; Han-Jong Jun. Deep learning-based natural language sentiment classification model for recognizing users’ sentiments toward residential space. Architectural Science Review 2020, 1 -12.
AMA StyleSunwoo Chang, Won-Hyeok Dong, Deuk-Young Rhee, Han-Jong Jun. Deep learning-based natural language sentiment classification model for recognizing users’ sentiments toward residential space. Architectural Science Review. 2020; ():1-12.
Chicago/Turabian StyleSunwoo Chang; Won-Hyeok Dong; Deuk-Young Rhee; Han-Jong Jun. 2020. "Deep learning-based natural language sentiment classification model for recognizing users’ sentiments toward residential space." Architectural Science Review , no. : 1-12.