• International Journal of Computer Science & Network Security
• /
• 제24권9호
• /
• pp.150-156
• /
• 2024

With the advent of personalized search engines, a myriad of approaches came into practice. With social media emergence the personalization was extended to different level. The main reason for this preference of personalized engine over traditional search was need of accurate and precise results. Due to paucity of time and patience users didn't want to surf several pages to find the result that suits them most. Personalized search engines could solve this problem effectively by understanding user through profiles and histories and thus diminishing uncertainty and ambiguity. But since several layers of personalization were added to basic search, the response time and resource requirement (for profile storage) increased manifold. So it's time to focus on optimizing the layered architectures of personalization. The paper presents a layout of the multi agent based personalized search engine that works on histories and profiles. Further to store the huge amount of data, distributed database is used at its core, so high availability, scaling, and geographic distribution are built in and easy to use. Initially results are retrieved using traditional search engine, after applying layer of personalization the results are provided to user. MongoDB is used to store profiles in flexible form thus improving the performance of the engine. Further Weighted Sum model is used to rank the pages in personalization layer.

• 경영과학
• /
• 제17권3호
• /
• pp.19-30
• /
• 2000

Customization and personalization services are considered as a critical success factor to be a successful Internet store or web service provider. As a representative personalization technique, personalized recommendation techniques are studied and commercialized to suggest products or services to a customer of Internet storefronts based on demographics of the customer or based on an analysis of the past purchasing behavior of the customer. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customers data. however, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed knowledge base. In this paper, we proposed a marketing rule extraction technique for personalized recommendation on Internet storefronts using market basket analysis technique, a well-known data mining technique. Using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store. An experiment has been performed to evaluate the effectiveness of proposed approach comparing with preference scoring approach and random selection.

• 한국의류산업학회지
• /
• 제9권2호
• /
• pp.188-196
• /
• 2007

Fashion business is undergoing a severe competition because of the various consumers' needs, over production, and short product life styles. The purpose of this study is to investigate the role of personalization in apparel shopping. Characterizing the notion of the personalization typology, investigating the effect of personalization to different consumers, and providing useful issues to draw more target consumers are to be accessed. For empirical research a survey method was employed. A measure for personalization in apparel shopping was developed based in existing scale items and pilot study. Consumers responded highly on design personalization in shopping apparel products. Factor analysis extracted six dimensions of apparel product personalization. Six types of personalization were: "personalized advice", "personalized design choice", "personalized fit", "sales-promotion personalization", "personalized costomer relationship management" and "in-store personalization".

• 한국경영정보학회:학술대회논문집
• /
• 한국경영정보학회 2008년도 춘계학술대회
• /
• pp.153-170
• /
• 2008

Customers' needs change every moment. Profitability of stores can't be increased anymore with an existing standardized chain store management. Accordingly, a personalized store management tool needs through prediction of customers' preference. In this study, we propose a recommending procedure using dynamic customers' preference by analyzing the transaction database. We utilize self-organizing map algorithm and association rule mining which are applied to cluster the chain stores and explore purchase sequence of customers. We demonstrate that the proposed methodology makes an effect on recommendation of products in the market which is characterized by a fast fashion and a short product life cycle.

• Genomics & Informatics
• /
• 제15권1호
• /
• pp.19-27
• /
• 2017

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data.

• 한국의류학회지
• /
• 제35권5호
• /
• pp.587-600
• /
• 2011

This study presents the strategic directions for Korean multi-brand fashion stores by running in-depth industry research and market analysis. Over 20 professionals were selected from Korean multi-brand fashion stores for this study and in-depth interviews were conducted to evaluate related subjects. The results of the study were as follows. First, Korean multi-brand fashion stores could be classified into three criteria: operating ownership, merchandise mix, and store identity. Second, operating ownership criterion was chosen for further investigations of strategies and directions of the multi-brand fashion stores. The operating ownership criterion consists of three types; department store types, specialty store types, and boutique types. Each type deploys different buying practice, organizational strategies, and distribution channels. Lastly, the suggested strategic directions for each type are summarized as follows. The 'department store type' should utilize its strong direct buying capabilities and acquisition of merchandising can be more effectively managed. The store should utilize its strong buying power as a tool to develop new private brands (PBs). For 'specialty store type', two key factors have been derived: market share expansion and positioning themselves to become a new distribution channel. To respond to these factors, the store needs to be perceived as a brand then diversify its business. Strengthening its brand will allow it to expand into a new distribution channel and also enable a strategic partnership with its competitor brands. The factors influencing 'boutique type' is personalization and uniqueness. With an emphasis on the uniqueness of products and merchandising it will be able to implement the role as a personal shopper and stylist to provide a very personalized service to its customers.

• 대한인간공학회지
• /
• 제23권3호
• /
• pp.1-12
• /
• 2004

In recent years, with the advent of e-Commerce the need for personalized services and one-to-one marketing has been emphasized. To be successful in increasingly competitive Internet marketplace, it is essential to capture customer loyalty. In this paper, we provide an intelligent agent approach to incorporate human sensibility into an one-to-one recommendation service in cyber shopping mall. Our system exploits human sensibility ergonomics and on-line preference matching technologies to tailor to the customer the suggestion of goods and the description of store catalog. Customizing the system`s behavior requires the parallel execution of several tasks during the interaction （e. g., identifying the customer`s emotional preference and dynamically generating the pages of the store catalog）. The recommendation agent system composed of five modules including specialized agents carries on these tasks. By presenting goods that are consistent with user interests as well as user sensibility, the accuracy and satisfaction of the recommendation service may be improved.

• 산경연구논집
• /
• 제6권4호
• /
• pp.5-16
• /
• 2015

Purpose - Currently, organizations must have a plan to achieve their future objectives. In this case, an information strategy facilitates greater success when planning for the future in any organization. Research design, data, and methodology - The core objective of the project was to explore the information infrastructure of Walkbase in a discursive manner. We started the project by providing a description of the firm, which facilitates retail outlets using in-store analytical devices. Results - We conclude that the management of Walkbase revised its current information structure to implement a more structured one that might be included in a long-term investment. On such an occasion, management can prioritize the component to develop first. Conclusions - Along with our results, we also described the business, its products, its facilities, and how it can serve different industries. Finally, we left the prioritization decision within the framework's components to top management.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
• /
• pp.770-773
• /
• 2005

In most biological laboratory, sequences from sequence machine are stored into file disks as simple files. It will be hard work to store and manage the sequence data with consistency and integrity such as storing redundant files. It is required needed to develop a system which integrated and managed genome data with consistency and integrity for accurate sequence analysis. There fore, in this paper, we not only store gene and protein sequence data through sequencing but also manage them. We also make a integrate schema for transforming the file formats and design database system using it. As integrated schema is designed as a BSML, it is possible to apply a style language of XSL. From this, we can transfer among heterogeneous sequence formats.

• 한국조리학회지
• /
• 제15권3호
• /
• pp.248-261
• /
• 2009

본 연구는 대형 할인점의 선택 속성이 소비자 구매 후 행동에 미치는 영향을 살펴봄으로써 대형 할인점들의 고객 만족 전략 수립에 필요한 기초 자료를 제공하고자 하였다. 본 연구결과 다음과 같은 사실이 밝혀졌다. 첫째, 상품 관련 요인은 구매 후 행동에 긍정적인 영향을 미치는 것으로 나타났다. 둘째, 서비스 관련 요인은 구매 후 행동에 긍정적인 영향을 미치는 것으로 나타났다. 셋째, 매장 관련 요인은 구매 후 행동에 긍정적인 영향을 미치는 것으로 나타났다. 이상과 같은 결과를 볼 때, 대형 할인점은 다양하고 개별화된 서비스를 제공함으로써 소비자로부터 신뢰를 얻고, 이용자가 주변에게 소개하는 효과를 얻을 수 있도록 해야 할 것이다.