• Title/Summary/Keyword: Large Shopping Mall

Search Result 67, Processing Time 0.024 seconds

Development of Supervised Machine Learning based Catalog Entry Classification and Recommendation System (지도학습 머신러닝 기반 카테고리 목록 분류 및 추천 시스템 구현)

  • Lee, Hyung-Woo
    • Journal of Internet Computing and Services
    • /
    • v.20 no.1
    • /
    • pp.57-65
    • /
    • 2019
  • In the case of Domeggook B2B online shopping malls, it has a market share of over 70% with more than 2 million members and 800,000 items are sold per one day. However, since the same or similar items are stored and registered in different catalog entries, it is difficult for the buyer to search for items, and problems are also encountered in managing B2B large shopping malls. Therefore, in this study, we developed a catalog entry auto classification and recommendation system for products by using semi-supervised machine learning method based on previous huge shopping mall purchase information. Specifically, when the seller enters the item registration information in the form of natural language, KoNLPy morphological analysis process is performed, and the Naïve Bayes classification method is applied to implement a system that automatically recommends the most suitable catalog information for the article. As a result, it was possible to improve both the search speed and total sales of shopping mall by building accuracy in catalog entry efficiently.

Analysis of Leasing Decision Determinants by the Store Size and Lend-Lease Perspectives for Mix-Used Shopping Mall Development (복합쇼핑몰 개발을 위한 매장규모 및 임대차 관점에 따른 임차인 입점결정요인에 관한 연구)

  • Park, Hyeyoon;Lee, Sangyoub
    • Korean Journal of Construction Engineering and Management
    • /
    • v.18 no.2
    • /
    • pp.49-57
    • /
    • 2017
  • This study intends to determine the decision making criteria of leasing in mix-used shopping mall, analyze the variation of their weight by store size inside mall and lend-lease perspective for lessor and lessee towards the identification of optimal leasing environment in mix-used shopping mall development. The decision making have been identified based on the number of prior literature review and expert consultation. And the AHP methodology and Fuzzy theory have been implemented to develop the weight for criteria based on experts survey. Research finding indicates that the 2 categories with 6 criteria and 24 sub-criteria have been determined. It is noteworthy that the large sized group would be located in both ends on main floor with their requested store size; middle sized group done over second floor with low rent by attracting with both competitive brands and key-tenants; small sized group done in both ends on main floor or on 2 to 3 floor connecting to main circulation. This should be examined in the planning stage of SPA lessee solicitation by the lessor in mix-used shopping mall development project.

SPACE STRUCTURE ANALYSIS OF COMPLEX CULTURE SHOPPING FACILITY.

  • Jae-Hong Hwang;Byung-ju Ank;Whoi-yul Kim;Jae-Joon Kim
    • International conference on construction engineering and project management
    • /
    • 2009.05a
    • /
    • pp.1128-1133
    • /
    • 2009
  • Recently super complex culture shopping facility development seeks consumer' convenience present and are coming restaurant neighborhood, cinema, shopping, hotel etc, according to intensive plan. Such as complex culture shopping facility was developed to most subway station area center and have concept that is space for a main facilities or space for environment protection, citizens' a rest in city. Howeve,r space of recently domestic large size complex culture shopping facility that do not plan systematically was lacking and caused result that do not use efficiently space. Limited extent of research that define complex culture shopping equipment and analyze form of space and present space planning with analysis of research connected with complex usage development.

  • PDF

Product Recommendation System on VLDB using k-means Clustering and Sequential Pattern Technique (k-means 클러스터링과 순차 패턴 기법을 이용한 VLDB 기반의 상품 추천시스템)

  • Shim, Jang-Sup;Woo, Seon-Mi;Lee, Dong-Ha;Kim, Yong-Sung;Chung, Soon-Key
    • The KIPS Transactions:PartD
    • /
    • v.13D no.7 s.110
    • /
    • pp.1027-1038
    • /
    • 2006
  • There are many technical problems in the recommendation system based on very large database(VLDB). So, it is necessary to study the recommendation system' structure and the data-mining technique suitable for the large scale Internet shopping mail. Thus we design and implement the product recommendation system using k-means clustering algorithm and sequential pattern technique which can be used in large scale Internet shopping mall. This paper processes user information by batch processing, defines the various categories by hierarchical structure, and uses a sequential pattern mining technique for the search engine. For predictive modeling and experiment, we use the real data(user's interest and preference of given category) extracted from log file of the major Internet shopping mall in Korea during 30 days. And we define PRP(Predictive Recommend Precision), PRR(Predictive Recommend Recall), and PF1(Predictive Factor One-measure) for evaluation. In the result of experiments, the best recommendation time and the best learning time of our system are much as O(N) and the values of measures are very excellent.

An Investigation on Expanding Co-occurrence Criteria in Association Rule Mining (온라인 연관관계 분석의 장바구니 기준에 대한 연구)

  • Kim, Mi-Sung;Kim, Nam-Gyu
    • CRM연구
    • /
    • v.4 no.2
    • /
    • pp.19-29
    • /
    • 2011
  • There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems.

  • PDF

A Design and Implementation of Needs Analysis System in Internet Shopping Mall (인터넷 쇼핑몰 니즈 분석 시스템의 설계 및 구현)

  • Park, Sung-hoon;Kim, Jindeog
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.19 no.9
    • /
    • pp.2073-2080
    • /
    • 2015
  • Even though users choose goods they want to buy in on-line shopping malls, real purchase is often performed in off-line shopping malls. It is called reverse showrooming. It means that users' analysis of goods based on images and description of internet shopping malls has limitation. Thus, large-scale online shopping malls provide a customized shopping information. However, in that case, the provided information is a simple list of goods users bought or retrieved. Thus, a system to analyze various needs of users and apply the result into on-line shopping mall is necessary. In this paper, an analysis system is proposed. The system contains a module to analyze user defined preference and a module to analyze users' reviews. The former designates two goods and collects preferences of individual users. the latter analyzes reviews about purchased goods based on database dictionary stored in advance for analyzing reviews. The system implemented shows that it is possible to recommend some goods that meet each users's needs

Modeling and simulation of large crowd evacuation in hazard-impacted environments

  • Datta, Songjukta;Behzadan, Amir H.
    • Advances in Computational Design
    • /
    • v.4 no.2
    • /
    • pp.91-118
    • /
    • 2019
  • Every year, many people are severely injured or lose their lives in accidents such as fire, chemical spill, public pandemonium, school shooting, and workplace violence. Research indicates that the fate of people in an emergency situation involving one or more hazards depends not only on the design of the space (e.g., residential building, industrial facility, shopping mall, sports stadium, school, concert hall) in which the incident occurs, but also on a host of other factors including but not limited to (a) occupants' characteristics, (b) level of familiarity with and cognition of the surroundings, and (c) effectiveness of hazard intervention systems. In this paper, we present EVAQ, a simulation framework for modeling large crowd evacuation by taking into account occupants' behaviors and interactions during an emergency. In particular, human's personal (i.e., age, gender, disability) and interpersonal (i.e., group behavior and interactions) attributes are parameterized in a hazard-impacted environment. In addition, different hazard types (e.g., fire, lone wolf attacker) and propagation patterns, as well as intervention schemes (simulating building repellent systems, firefighters, law enforcement) are modeled. Next, the application of EVAQ to crowd egress planning in an airport terminal under human attack, and a shopping mall in fire emergency are presented and results are discussed. Finally, a validation test is performed using real world data from a past building fire incident to assess the reliability and integrity of EVAQ in comparison with existing evacuation modeling tools.

Customer Classification and Market Basket Analysis Using K-Means Clustering and Association Rules: Evidence from Distribution Big Data of Korean Retailing Company (군집분석과 연관규칙을 활용한 고객 분류 및 장바구니 분석: 소매 유통 빅데이터를 중심으로)

  • Liu, Run-Qing;Lee, Young-Chan;Mu, Hong-Lei
    • Knowledge Management Research
    • /
    • v.19 no.4
    • /
    • pp.59-76
    • /
    • 2018
  • With the arrival of the big data era, customer data and data mining analysis have gradually dominated the process of Customer Relationship Management (CRM). This phenomenon indicates that customer data along with the use of information techniques (IT) have become the basis for building a successful CRM strategy. However, some companies can not discover valuable information through a large amount of customer data, which leads to the failure of making appropriate business strategy. Without suitable strategies, the companies may lose the competitive advantage or probably go bankrupt. The purpose of this study is to propose CRM strategies by segmenting customers into VIPs and Non-VIPs and identifying purchase patterns using the the VIPs' transaction data and data mining techniques (K-means clustering and association rules) of online shopping mall in Korea. The results of this paper indicate that 227 customers were segmented into VIPs among 1866 customers. And according to 51,080 transactions data of VIPs, home product and women wear are frequently associated with food, which means that the purchase of home product or women wears mainly affect the purchase of food. Therefore, marketing managers of shopping mall should consider these shopping patterns when they build CRM strategy.

Trace Element Analysis and Source Assessment of Parking Lot Dust in Large Shopping Mall (대형유통업소주차장의 축적먼지 중 미량원소성분 분석과 오염원 평가)

  • Song, Hee-Bong;Ahn, Jeong-Eem;Jung, Yeoun-Wook;Yoon, Ho-Suk;Keum, Jong-Lok;Do, Hwa-Seok;Kim, Sun-Suk;Kim, Jong-Woo
    • Journal of Korean Society of Environmental Engineers
    • /
    • v.34 no.3
    • /
    • pp.168-176
    • /
    • 2012
  • A total of 48 dust samples were collected from large shopping mall parking lots in Daegu metropolitan city in March 2011. Samples were sieved through a 100 ${\mu}m$ mesh and the concentration of 14 elements have been determined using by ICP after acid extraction. Results showed that Ca, Fe, K, Mg, Mn, Na and V were affected by natural sources while Cd, Cr, Cu, Ni, Pb and Zn were affected by anthropogenic sources. The measured values were remarkably higher in components from natural sources than in components from anthropogenic sources. Anthropogenic trace element concentrations of ground roof dust were higher than those of ground and underground indoor dust. A large percentage of trace elements came from natural sources rather than anthropogenic sources. The percentage composition of chemicals of ground roof dust were higher than those of ground and underground indoor dust. This study showed that investigated parking lots were rarely contaminated with hazardous heavy metals. The heavy metal pollution of ground roof were higher than those of ground and underground indoors. The correlation analysis among trace elements suggest that components in ground roof were more highly correlated than those in ground and underground indoor. Also anthropogenic trace element levels were well correlated with parking lot age and parking density.

Analyzing the weblog data of a shopping mall using process mining (프로세스 마이닝을 이용한 쇼핑몰 웹로그 데이터 분석)

  • Kim, Chae-Young;Yong, Hye-Ryeon;Hwang, Hyun-Seok
    • Journal of the Korea Academia-Industrial cooperation Society
    • /
    • v.21 no.11
    • /
    • pp.777-787
    • /
    • 2020
  • With the development of the Internet and the spread of mobile devices, the online market is growing rapidly. As the number of customers using online shopping malls explodes, research is being conducted on the analysis of usage behavior from customer data, personalized product recommendations, and service development. Thus, this paper seeks to analyze the overall process of online shopping malls through process mining, and to identify the factors that influence users' purchases. The data used are from a large online shopping mall, and R was the analysis tool. The results show that customer activity was most prominent in categories with event elements, such as unconventional discounts and monthly giveaway events. On the other hand, searches, logins, and campaign activity were found to be less relevant than their importance. Those are very important, because they can provide clues to a customer's information and needs. Therefore, it is necessary to refine the recommendations from related search words, and to manage activity, such as coupons provided when customers log in. In addition to the previous discussion, this paper proposes various business strategies to enhance the competitiveness of online shopping malls and to increase profits.