• Information Systems Review
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• v.23 no.3
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• pp.51-75
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• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• Korean Management Science Review
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• v.17 no.3
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• pp.19-30
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• 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.

• Journal of Information Technology Services
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• v.12 no.4
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• pp.1-23
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• 2013

This study examines whether key characteristics of cloud computing services would affect the intention of use for personalized cloud computing services. The research model was generated based on Technology Acceptance Model (TAM) with resistance variable, and verified statistically by undertaking a survey about the perception of personal users. As the results of this analysis, we could find the structural relationship among the factors affecting adoption of the cloud computing service. We found that the expectation of ubiquity as a representative function of the cloud computing service meaningfully affected the perceived ease of use and resistance, and that the relativeness with existing services also meaningfully affected the perceived ease of use, but not the resistance. In addition, the moderating effects of use experience in the path leading from the perceived ease of use and resistance to the intention of use were identified. This study will provide diverse implications for the companies providing personalized cloud computing services.

• Journal of the Korea Computer Industry Society
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• v.2 no.8
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• pp.1109-1116
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• 2001

This paper describes a mechanism and its aspects for monitoring user actions and behaviors on the web operated under the layered architecture that utilizes intelligent agents, and by which personalized information services, for instance one-to-one marketing, is easily facilitated and developed. Intelligent agent is one of techniques which enables to automate the whole process for providing personalized services that includes monitoring, logging a user actions and analyzing behaviors based on his or her profiles, and then selecting, organizing customizable contents which are at last delivered to the users browser or mail box also by intelligent agents. Monitoring technologies can utilize to help better construct the process in providing personalized services in that those services require intelligence in operating. As a result, implementing effective personalized services, however, depends on how well to define various users interests and needs, and how correctly to detect and log the actions when hey are done by users.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.441-447
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• 2017

In order to provide personalized services on the Web and for mobile services, it is necessary to collect and analyze information processed by users. Typically, information collected by users is managed online. Using information collected online may be sufficient to provide personalized service. However, in terms of O2O services, which are currently mixed with online and offline services, user information from the offline service can also be an important part of personalized service. Therefore, this study suggests an architecture to collect offline user information to provide more precise personalization services. The collection architecture includes Node Analyzer, Distance Checker, Holding Time Checker, and Cross Analyzer as core elements. This study also offers proposals for processing algorithms of key components that make up the proposed architecture. A case study collects user information of interest based on BLE in order to verify the proposed architecture and algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2016-2034
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• 2011

Ubiquitous Healthcare (u-Healthcare) is the intelligent delivery of healthcare services to users anytime and anywhere. To provide robust healthcare services, recognition of patient daily life activities is required. Context information in combination with user real-time daily life activities can help in the provision of more personalized services, service suggestions, and changes in system behavior based on user profile for better healthcare services. In this paper, we focus on the intelligent manipulation of activities using the Context-aware Activity Manipulation Engine (CAME) core of the Human Activity Recognition Engine (HARE). The activities are recognized using video-based, wearable sensor-based, and location-based activity recognition engines. An ontology-based activity fusion with subject profile information for personalized system response is achieved. CAME receives real-time low level activities and infers higher level activities, situation analysis, personalized service suggestions, and makes appropriate decisions. A two-phase filtering technique is applied for intelligent processing of information (represented in ontology) and making appropriate decisions based on rules (incorporating expert knowledge). The experimental results for intelligent processing of activity information showed relatively better accuracy. Moreover, CAME is extended with activity filters and T-Box inference that resulted in better accuracy and response time in comparison to initial results of CAME.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.426-439
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• 2021

To alleviate the cold-start problem and data sparsity in web service recommendation and meet the personalized needs of users, this paper proposes a personalized web service recommendation method based on a hybrid social network and multi-objective immune optimization. The network adds the element of the service provider, which can provide more real information and help alleviate the cold-start problem. Then, according to the proposed service recommendation framework, multi-objective immune optimization is used to fuse multiple attributes and provide personalized web services for users without adjusting any weight coefficients. Experiments were conducted on real data sets, and the results show that the proposed method has high accuracy and a low recall rate, which is helpful to improving personalized recommendation.

• Journal of Broadcast Engineering
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• v.9 no.4 s.25
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• pp.345-356
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• 2004

In this paper, we present a design and implementation of a personalized broadcasting system using TV-Anytime metadata for providing personalized services. The TV-Anytime specifies metadata schema, metadata coding and delivery, and provides service models to provide personalized broadcasting content services at anytime when users want to consume using metadata, which includes ECG (Electronic Content Guide) and content descriptive information in a PDR (Personal Digital Recorder)-centric environment. The proposed personalized broadcasting system consists of a server that provides metadata binary-coding, encapsulation and multiplexing, and a client terminal that takes charge of de-multiplexing, metadata decoding, and metadata processing for personalized content accessing and consumption. This paper presents the details of the design of each functional module, and the evaluation results with a set of service scenarios in an end-to-end broadcasting test-bed.

• Journal of Broadcast Engineering
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• v.10 no.3
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• pp.422-435
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• 2005

Compared with analog broadcasting, digital broadcasting supports technical background to provide personalize the TV watching environment by offering broadcasting services that can adapt to viewers' preferences. However, current digital broadcasting shows limited services such as reservation recording, simple program guiding with an electronic program guide (EPG) on a personal video recorder system, and primitive data broadcasting by broadcasters. Therefore, the purpose of this paper is to suggest a new broadcasting environment which gives a person facility and a difference fur watching TV by serving enhanced personalized services. For that reason, we propose an intelligent broadcasting system which can minimize viewer's actions, and enhanced broadcasting services which are based on understanding of the semantics of broadcasting contents. To implement the system, agent technology as well as the MPEG-7 and TV-Anytime Forum (TVAF) are employed. For content-level services, real-time content filtering and personalized video skimming are designed and implemented. To verify the usefulness of the proposed system, we demonstrate it with a test-bed on which content-level personalized services are implemented.

• Asia pacific journal of information systems
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• v.14 no.3
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• pp.121-144
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• 2004

Developing a personalized system on a user's behalf which is working around the Internet-based marketplace is one of the challenging issues in intelligent e-business, especially mobile commenrce. It has been highly recommended that such a mobile personalized system has to perceive the user's needs a priori by tracking user's current context such as location with activity and then to identify the current needs dynamically and proactively. Automatically and unobtrusively getting user's context is an inevitable feature for the development of autonomous mobile commenrce. However, personalization methodologies and their feasible architectures for context-aware mobile commerce have been so far very rare. Hence, this paper aims to propose a context-aware mobile commerce development methodology by applying agent and semantic web technologies for personalized reminder system, which is one of the mobile commerce support system. We revisited associationism to understand a buyer's need identification process and adopt the process as 'purchase based on association' to implement a personalized reminder system. Based on this approach, we have showed how the agent-based semantic web service system can be used to realize need-aware reminder system. NAMA(Need-Aware Multi-Agent), a prototype system, has been implemented to show the feasibility of the methodology and framework under mobile setting proposed in this paper. NAMA embeds bluetooth-based location tracking module and identify what a user is currently looking at through her/his mobile device such as PDA. Based on these capabilities, NAMA considers the context, user profile with preferences, and information about currently available services, to aware user's current needs and then link her/him to a set of services, which are implemented as web services.