Browsing by Author "Lai, Chin-Hui"

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A hybrid of sequential rules and collaborative filtering for product recommendation
(2007)
Liu, Duen-Ren
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Lai, Chin-Hui
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Lee, Wang-Jung
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Customers' purchase behavior may vary over time. Traditional collaborative filtering (CF) methods make recommendations to a target customer based on the purchase behavior of customers whose preferences are similar to those of the target customer,- however, the methods do not consider how the customers' purchase behavior may vary over time. Although the sequential rule method considers the sequence of customers' purchase behavior over time, it does not make use of the target customer's purchase data for the current period. To resolve the above problems, this work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentation-based CF method. Experiment. results show that the hybrid method outperforms traditional CF methods. Keywords:
A hybrid of sequential rules and collaborative filtering for product recommendation
(2009-09-29)
Liu, Duen-Ren
;
Lai, Chin-Hui
;
Lee, Wang-Jung
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Customers' purchase behavior may vary over time. Traditional collaborative filtering (CF) methods make recommendations to a target customer based on the purchase behavior of customers whose preferences are similar to those of the target customer; however, the methods do not consider how the customers' purchase behavior may vary over time. In contrast, the sequential rule-based recommendation method analyzes customers' purchase behavior over time to extract sequential rules in the form: purchase behavior in previous periods double right arrow purchase behavior in the current period. If a target customer's purchase behavior history is similar to the conditional part of the rule, then his/her purchase behavior in the current period is deemed to be the consequent part of the rule. Although the sequential rule method considers the sequence of customers' purchase behavior over time, it does not utilize the target customer's purchase data for the current period. To resolve the above problems, this work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentation-based KNN-CF method. The proposed method uses customers' RFM (Recency, Frequency, and Monetary) values to cluster customers into groups with similar RFM values. For each group of customers, sequential rules are extracted from the purchase sequences of that group to make recommendations. Meanwhile, the segmentation-based KNN-CF method provides recommendations based on the target customer's purchase data for the current period. Then, the results of the two methods are combined to make final recommendations. Experiment results show that the hybrid method outperforms traditional CF methods. (C) 2009 Elsevier Inc. All rights reserved.
A social recommendation method based on the integration of social relationship and product popularity
(2019-01-01)
Lai, Chin-Hui
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Lee, Shin-Jye
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Huang, Hung-Ling
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科技管理研究所
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Institute of Management of Technology
Web 2.0 technology fosters the flourishing growth and development of social networks. More and more people are participating in the activities on social networks to interact and share information with each other. Thus, consumers are often making their purchasing decisions based on information from the Internet such as reviews, ratings, and comments on products, especially from their trusted friends. However, a great amount of available information may cause the problem of information overload for consumers. In seeking to attain a good recommendation performance by taking the high-potential factors into account as far as possible, this paper proposes a novel social recommendation method on the basis of the integration of interactions, trust relationships and product popularity to predict user preferences, and recommend relevant products in social networks. In addition, the proposed method mainly focuses on analyzing user interactions to infer their latent interactions in accordance with the user ratings and corresponding reviews. Additionally, users may be affected by the popularity of products, so this factor has also been taken into consideration in this work. The experimental results show that the proposed recommendation method has a better recommendation performance in comparisons to other methods because the proposed method can accurately analyze user preferences and further recommend high-potential products to target users in social networks to support their purchase decision making. Furthermore, the proposed method can not only reduce the time and effort users spend on querying information, but also positively relieve the problem of information overload.
A trustworthy QoS-based collaborative filtering approach for web service discovery
(2014-07-01)
Lin, Szu-Yin
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Lai, Chin-Hui
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Wu, Chih-Heng
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Lo, Chi-Chun
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資訊管理與財務金融系註：原資管所+財金所
Many network services which process a large quantity of data and knowledge are available in the distributed network environment, and provide applications to users based on Service-Oriented Architecture (SOA) and Web services technology. Therefore, a useful web service discovery approach for data and knowledge discovery process in the complex network environment is a very significant issue. Using the traditional keyword-based search method, users find it difficult to choose the best web services from those with similar functionalities. In addition, in an untrustworthy real world environment, the QoS-based service discovery approach cannot verify the correctness of the web services\' Quality of Service (QoS) values, since such values guaranteed by a service provider are different from the real ones. This work proposes a trustworthy two-phase web service discovery mechanism based on QoS and collaborative filtering, which discovers and recommends the needed web services effectively for users in the distributed environment, and also solves the problem of services with incorrect QoS information. In the experiment, the theoretical analysis and simulation experiment results show that the proposed method can accurately recommend the needed services to users, and improve the recommendation quality. (C) 2014 Elsevier Inc. All rights reserved.
A Trustworthy QoS-based Mechanism for Web Service Discovery Based on Collaborative Filtering
(2013)
Lin, Szu-Yin
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Lai, Chin-Hui
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Wu, Chih-Heng
;
Lo, Chi-Chun
;
資訊管理與財務金融系註：原資管所+財金所
Many network services which process a large quantity of data and knowledge are available in the distributed network environment, and provide applications to users based on Service-Oriented Architecture (SOA) and Web services technology. Therefore, a useful web service discovery approach for data and knowledge discovery process in the complex network environment is a very significant issue. U sing the traditional keyword-based search method, users find it difficult to choose the best web services from those with similar functionalities. In addition, in an untrustworthy real world environment, the QoS-based service discovery approach cannot verify the correctness of the web services\' Quality of Service (QoS) values, since such values guaranteed by a service provider are different from the real ones. This work proposes a trustworthy two-phase web service discovery mechanism based on QoS and collaborative filtering, which discovers and recommends the needed web services effectively for users in the distributed environment, and also solves the problem of services with incorrect QoS information. In the experiment, the theoretical analysis and simulation experiment results show that the proposed method can accurately recommend the needed services to users, and improve the recommendation quality.
Document recommendation for knowledge sharing in personal folder environments
(2008-08-01)
Liu, Duen-Ren
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Lai, Chin-Hui
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Huang, Chiu-Wen
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資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Sharing sustainable and valuable knowledge among knowledge workers is a fundamental aspect of knowledge management. In organizations, knowledge workers usually have personal folders in which they organize and store needed codified knowledge (textual documents) in categories. In such personal folder environments, providing knowledge workers with needed knowledge from other workers' folders is important because it increases the workers' productivity and the possibility of reusing and sharing knowledge. Conventional recommendation methods can be used to recommend relevant documents to workers; however, those methods recommend knowledge items without considering whether the items are assigned to the appropriate category in the target user's personal folders. In this paper, we propose novel document recommendation methods, including content-based filtering and categorization, collaborative filtering and categorization, and hybrid methods, which integrate text categorization techniques, to recommend documents to target worker's personalized categories. Our experiment results Show that the hybrid methods Outperform the pure content-based and the collaborative filtering and categorization methods. The proposed methods not only proactively notify knowledge workers about relevant documents held by their peers, but also facilitate push-mode knowledge sharing. (C) 2007 Elsevier Inc. All rights reserved.
Document Recommendation with Implicit Feedback Based on Matrix Factorization and Topic Model
(2018-01-01)
Lai, Chin-Hui
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Liu, Duen-Ren
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Lin, Siao-Rong
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資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Recommender systems have been applied in many domains to solve the information-overload problem, and most of them make recommendations based on explicit data which expressed ratings in different scores. However, there are a lot of implicit data in the real world, such as users' purchase history, click history, browsing activity and so on, and it is difficult to find users' preferences based on this kind of data. In this work, we proposed a novel recommendation method, which incorporates topic model and matrix factorization. The content of documents and similar users' preferences are used to predict the negative and positive examples. The proposed approach achieves better performance than other recommender systems with implicit feedback.
Document recommendations based on knowledge flows: A hybrid of personalized and group-based approaches
(2012-10-01)
Liu, Duen-Ren
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Lai, Chin-Hui
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Chen, Ya-Ting
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資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Recommender systems can mitigate the information overload problem and help workers retrieve knowledge based on their preferences. In a knowledge-intensive environment, knowledge workers need to access task-related codified knowledge (documents) to perform tasks. A worker's document referencing behavior can be modeled as a knowledge flow (KF) to represent the evolution of his or her information needs over time. Document recommendation methods can proactively support knowledge workers in the performance of tasks by recommending appropriate documents to meet their information needs. However, most traditional recommendation methods do not consider workers KFs or the information needs of the majority of a group of workers with similar KFs. A group's needs may partially reflect the needs of an individual worker that cannot be inferred from his or her past referencing behavior. In other words, the group's knowledge complements that of the individual worker. Thus, we leverage the group perspective to complement the personal perspective by using hybrid approaches, which combine the KF-based group recommendation method (KFGR) with traditional personalized-recommendation methods. The proposed hybrid methods achieve a trade-off between the group-based and personalized methods by exploiting the strengths of both. The results of our experiment show that the proposed methods can enhance the quality of recommendations made by traditional methods.
Group Recommendation Based on the Analysis of Group Influence and Review Content
(2017-01-01)
Lai, Chin-Hui
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Hong, Pei-Ru
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資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
With the development of internet, users not only receive information passively but also share their own opinions on the social networking websites. Accordingly, users' preferences for items may be affected by others through opinion sharing and social interactions. Moreover, users with similar preferences usually form a group to share related information with others. Users' preferences may be affected by group members. Existing researches often focus on analyzing personal preferences and group recommendation approaches without user influence. In this work, we propose a novel group recommendation approach which combines the group influence, rating-based score and profile similarity to predict group preference. The group influence is composed of group member influences, review influence and recommendation influence. The profile similarity is derived from the analysis of item descriptions and review content. The experimental results show that considering the group influence and content information in group recommendation approach can effectively improve the recommendation performance.
Image Classification Based on the Boost Convolutional Neural Network
(2018-01-01)
Lee, Shin-Jye
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Chen, Tonglin
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Yu, Lun
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Lai, Chin-Hui
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科技管理研究所
Convolutional neural networks (CNNs), which are composed of multiple processing layers to learn the representations of data with multiple abstract levels, are the most successful machine learning models in recent years. However, these models can have millions of parameters and many layers, which are difficult to train, and sometimes several days or weeks are required to tune the parameters. Within this paper, we present the usage of a trained deep convolutional neural network model to extract the features of the images, and then, used the AdaBoost algorithm to assemble the Softmax classifiers into recognizable images. This method resulted in a 3% increase of accuracy of the trained CNN models, and dramatically reduced the retraining time cost, and thus, it has good application prospects.
Integrating knowledge flow mining and collaborative filtering to support document recommendation
(2009-12-01)
Lai, Chin-Hui
;
Liu, Duen-Ren
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Knowledge is a critical resource that organizations use to gain and maintain competitive advantages. In the constantly changing business environment, organizations Must exploit effective and efficient methods of preserving, sharing and reusing knowledge in order to help knowledge workers find task-relevant information. Hence, an important issue is how to discover and model the knowledge flow (KF) of workers from their historical work records. The objectives of a knowledge flow model are to understand knowledge workers' task-needs and the ways they reference documents, and then provide adaptive knowledge support. This work proposes hybrid recommendation methods based on the knowledge flow model, which integrates KF mining, sequential rule mining and collaborative filtering techniques to recommend codified knowledge. These KF-based recommendation methods involve two phases: a KF mining phase and a KF-based recommendation phase. The KF mining phase identifies each worker's knowledge flow by analyzing his/her knowledge referencing behavior (information needs), while the KF-based recommendation phase utilizes the proposed hybrid methods to proactively provide relevant codified knowledge for the worker. Therefore, the proposed methods use workers' preferences for codified knowledge as well as their knowledge referencing behavior to predict their topics of interest and recommend task-related knowledge. Using data collected from a research institute laboratory, experiments are conducted to evaluate the performance of the proposed hybrid methods and compare them with the traditional CF method. The results of experiments demonstrate that utilizing the document preferences and knowledge referencing behavior of workers can effectively improve the quality of recommendations and facilitate efficient knowledge sharing. (C) 2009 Elsevier Inc. All rights reserved.
Knowledge flow-based document recommendation for knowledge sharing
(2007)
Lai, Chin-Hui
;
Liu, Duen-Ren
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Knowledge is a critical property that organizations use to gain and maintain competitive advantages. In the constantly changing business environment, organizations have to exploit effective and efficient approaches to help knowledge workers find task-relevant knowledge, as well as to preserve, share and reuse such knowledge. Hence, an important issue is how to discover knowledge flow (KF) from the historical work records of knowledge workers in order to understand their task-needs and the ways they reference documents, and actively provide adaptive knowledge support. This work proposes a KF-based document recommendation method that integrates KF mining and collaborative filtering recommendation mechanisms to recommend codified knowledge. The approach consists of two phases: the KF mining phase and the recommendation phase. The KF mining phase can identify each worker's knowledge flow by considering the referencing time and citation relations of knowledge resources. Then, based on the discovered KF, the recommendation phase applies sequential rule mining and the CF method to recommend relevant documents to the target worker. Experiments are conducted to evaluate the performance of the proposed method and compare it with the traditional CF method using data collected from a research institute laboratory. The experiment results show that the proposed method can improve the quality of recommendation.
Mining group-based knowledge flows for sharing task knowledge
(2011-01-01)
Liu, Duen-Ren
;
Lai, Chin-Hui
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
In an organization, knowledge is the most important resource in the creation of core competitive advantages. It is circulated and accumulated by knowledge flows (KFs) in the organization to support workers' task needs. Because workers accumulate knowledge of different domains, they may cooperate and participate in several task-based groups to satisfy their needs. In this paper, we propose algorithms that integrate information retrieval and data mining techniques to mine and construct group-based KFs (GKFs) for task-based groups. A GKF is expressed as a directed knowledge graph which represents the knowledge referencing behavior, or knowledge flow, of a group of workers with similar task needs. Task-related knowledge topics and their relationships (flows) can be identified from the knowledge graph so as to fulfill workers' task needs and promote knowledge sharing for collaboration of group members. Moreover, the frequent knowledge referencing path can be identified from the knowledge graph to indicate the frequent knowledge flow of the workers. To demonstrate the efficacy of the proposed methods, we implement a prototype of the GKF mining system. Our GKF mining methods can enhance organizational learning and facilitate knowledge management, sharing, and reuse in an environment where collaboration and teamwork are essential. (C) 2010 Elsevier B.V. All rights reserved.
Mining knowledge flow for modeling the information needs of task-based groups
(2008)
Lai, Chin-Hui
;
Liu, Duen-Ren
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Knowledge is the most important resource to create core competitive advantages for an organization. Such knowledge is circulated and accumulated by a knowledge flow (KF) in an organization to support worker tasks. Workers may cooperate and participate in several task-based groups to fulfill their needs. In this paper, we propose a group-based knowledge flow mining algorithm which integrates information retrieval and data mining techniques for mining and constructing the group-based KF (GKF) for task-based groups. The GKF is expressed as a directed knowledge graph to represent the knowledge referencing behavior for a group of workers with similar task needs. The frequent knowledge referencing paths are identified from the knowledge graph to indicate the frequent knowledge flows of the workers. We also implement a prototype of GKF mining system to demonstrate the effectiveness of our proposed method.
Mining the change of event trends for decision support in environmental scanning
(2009-03-01)
Liu, Duen-Ren
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Shih, Meng-Jung
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Liau, Churn-Jung
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Lai, Chin-Hui
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資訊管理與財務金融系註：原資管所+財金所
As the business environment has become increasingly complex, the demand for environmental scanning to assist company managers plan strategies and responses has grown significantly. The conventional technique for supporting environmental scanning is event detection from text documents such as news stories. Event detection methods recognize events, but neglect to discover the changes brought about by the events. In this work, we propose an event change detection (ECD) approach that combines association rule mining and change mining techniques. The approach detects changes caused by events to help managers respond rapidly to changes in the external environment. Association rule mining is used to discover event trends (the subject patterns of events) from news stories. The changes can be identified by comparing event trends in different time periods. The empirical evaluation showed that the discovered event changes can support decision-makers by providing up-to-date information about the business environment, which enables them to make appropriate decisions. The proposed approach is practical for business managers to be aware of environmental changes and adjust their business strategies accordingly. (C) 2007 Elsevier Ltd. All rights reserved.
Novel personal and group-based trust models in collaborative filtering for document recommendation
(2013-08-01)
Lai, Chin-Hui
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Liu, Duen-Ren
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Lin, Cai-Sin
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資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Collaborative filtering (CF) recommender systems have been used in various application domains to solve the information-overload problem. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques in order to improve recommendation quality. Some researchers have proposed rating-based trust models to derive trust values based on users' past ratings of items, or based on explicitly specified relations (e.g. friends) or trust relationships; however, the rating-based trust model may not be effective in CF recommendations due to unreliable trust values derived from very few past rating records. In this work, we propose a hybrid personal trust model which adaptively combines the rating-based trust model and explicit trust metric to resolve the drawback caused by insufficient past rating records. Moreover, users with similar preferences usually form a group to share items (knowledge) with each other; thus, users' preferences may be affected by group members. Accordingly, group trust can enhance personal trust to support recommendations from the group perspective. We then propose a recommendation method based on a hybrid model of personal and group trust to improve recommendation performance. The experimental results show that the proposed models can improve the prediction accuracy of other trust-based recommender systems. (C) 2013 Elsevier Inc. All rights reserved.
Recommendations based on personalized tendency for different aspects of influences in social media
(2015-12-01)
Lai, Chin-Hui
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Liu, Duen-Ren
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Liu, Mei-Lan
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交大名義發表
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National Chiao Tung University
Among the applications of Web 2.0, social networking sites continue to proliferate and the volume of content keeps growing; as a result, information overload causes difficulty for users attempting to choose useful and relevant information. To resolve this problem, most researches only utilize users\' preferences, the content of items or social influence to make recommendations. However, people\'s preferences for items may be affected by social friends, personal interest and item popularity. Moreover, each factor has a different impact on each user. In this work, we propose a novel recommendation method based on different types of influences: social, interest and popularity, using personal tendencies in regard to these factors to recommend photos in a photo-sharing website, Flickr. The personal tendencies related to these three influences are regarded as personalized weights to combine influence scores for predicting the scores of items. The experimental results show that our proposed methods can improve the quality of recommendations.
RECOMMENDING DOCUMENTS VIA KNOWLEDGE FLOW-BASED GROUP RECOMMENDATION
(2011)
Lai, Chin-Hui
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Liu, Duen-Ren
;
Chen, Ya-Ting
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Recommender systems can mitigate the information overload problem and help workers retrieve knowledge based on their preferences. In a knowledge-intensive environment, knowledge workers need to access task related codified knowledge (documents) to perform tasks. A worker\'s document referencing behaviour can be modelled as a knowledge flow (KF) to represent the evolution of his/her information needs over time. Document recommendation methods can proactively support knowledge workers in the performance of tasks by recommending appropriate documents to meet their information needs. However, most traditional recommendation methods do not consider workers\' knowledge flows and the information needs of the majority of a group of workers with similar knowledge flows. A group\'s needs may partially reflect the needs of an individual worker that cannot be inferred from his/her past referencing behaviour. Thus, we leverage the group perspective to complement the personal perspective by using a hybrid approach, which combines the KF-based group recommendation method (KFGR) with the user-based collaborative filtering method (UCF). The proposed hybrid method achieves a trade-off between the group-based and the personalized method by integrating the merits of both methods. Our experiment results show that the proposed method can enhance the quality of recommendations made by traditional methods.
Sequence-Based Trust for Document Recommendation
(2009)
Chiu, Hsuan
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Liu, Duen-Ren
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Lai, Chin-Hui
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Collaborative Filtering (CF) recommender systems have emerged in various applications to Support item recommendation, which solve the information-overload problem by suggesting items of interests to users. Recently, trust-based recommender systems have incorporated the trustworthiness of users into the CF techniques to improve the quality of recommendation. They propose trust computation models to derive the trust value based on users' past ratings on items. A user is more trustworthy if he has contributed more accurate predictions than other users. Nevertheless, none of them derive the trust value based on a sequence of user's ratings on items. We propose a sequence-based trust model to derive the trust Value based on users' sequences of ratings on documents. In knowledge-intensive environments, users normally have various information needs to access required documents over time, which forms a sequence of documents ordered according to their access time. The model considers two factors time factor and document similarity in computing the trustworthiness Of users. The proposed model is incorporated into standard collaborative filtering method to discover trustworthy neighbors for making predictions. The experiment result shows that the proposed model can improve the prediction accuracy of CF method comparing to other trust-based recommender systems.
Sequence-based trust in collaborative filtering for document recommendation
(2011-08-01)
Liu, Duen-Ren
;
Lai, Chin-Hui
;
Chiu, Hsuan
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Collaborative filtering (CF) recommender systems have emerged in various applications to support item recommendation, which solve the information-overload problem by suggesting items of interest to users. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques to improve the quality of recommendation. They propose trust computation models to derive the trust values based on users' past ratings on items. A user is more trustworthy if s/he has contributed more accurate predictions than other users. Nevertheless, conventional trust-based CF methods do not address the issue of deriving the trust values based on users' various information needs on items over time. In knowledge-intensive environments, users usually have various information needs in accessing required documents over time, which forms a sequence of documents ordered according to their access time. We propose a sequence-based trust model to derive the trust values based on users' sequences of ratings on documents. The model considers two factors time factor and document similarity - in computing the trustworthiness of users. The proposed model enhanced with the similarity of user profiles is incorporated into a standard collaborative filtering method to discover trustworthy neighbors for making predictions. The experiment result shows that the proposed model can improve the prediction accuracy of CF method in comparison with other trust-based recommender systems. (C) 2011 Elsevier Ltd. All rights reserved.