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A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles
(2016-01-01)
Omar, Hani
;
Van Hai Hoang
;
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
Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words.
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.
Mobile commerce product recommendations based on hybrid multiple channels
(2011-01-01)
Liu, Duen-Ren
;
Liou, Chuen-He
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
The number of third generation (3G) subscribers conducting mobile commerce has increased as mobile data communications have evolved. Multi-channel companies that wish to develop mobile commerce face difficulties due to the lack of knowledge about users' consumption behavior on new mobile channels. Typical collaborative filtering (CF) recommendations may be affected by the so-called sparsity problem because relatively few products are browsed or purchased on the mobile Web. In this study, we propose a hybrid multiple channel method to address the lack of knowledge about users' consumption behavior on a new channel and the difficulty of finding similar users due to the sparsity problem of typical CF recommender systems. Products are recommended to users based on their browsing behavior on the new mobile channel as well as the consumption behavior of heavy users of existing channels, such as television, catalogs, and the Web. Our experiment results show that the proposed method performs well compared to the other recommendation methods. (C) 2010 Elsevier B.V. All rights reserved.
Hybrid-Patent Classification Based on Patent-Network Analysis
(2011-02-01)
Liu, Duen-Ren
;
Shih, Meng-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
Effective patent management is essential for organizations to maintain their competitive advantage. The classification of patents is a critical part of patent management and industrial analysis. This study proposes a hybrid-patent-classification approach that combines a novel patent-network-based classification method with three conventional classification methods to analyze query patents and predict their classes. The novel patent network contains various types of nodes that represent different features extracted from patent documents. The nodes are connected based on the relationship metrics derived from the patent metadata. The proposed classification method predicts a query patent's class by analyzing all reachable nodes in the patent network and calculating their relevance to the query patent. It then classifies the query patent with a modified k-nearest neighbor classifier. To further improve the approach, we combine it with content-based, citation-based, and metadata-based classification methods to develop a hybrid- classification approach. We evaluate the performance of the hybrid approach on a test dataset of patent documents obtained from the U.S. Patent and Trademark Office, and compare its performance with that of the three conventional methods. The results demonstrate that the proposed patent-network-based approach yields more accurate class predictions than the patent network-based approach.
Recommending QA Documents for Communities of Question-Answering Websites
(2013)
Liu, Duen-Ren
;
Huang, Chun-Kai
;
Chen, Yu-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
Question & Answering (Q&A) websites have become an essential knowledge-sharing platform. This platform provides knowledge-community services where users with common interests or expertise can form a knowledge community to collect and share QA documents. However, due to the massive amount of QAs, information overload can become a major problem. Consequently, a recommendation mechanism is needed to recommend QAs for communities of Q&A websites. Existing studies did not investigate the recommendation mechanisms for knowledge collections in communities of Q&A Websites. In this work, we propose a novel recommendation method to recommend related QAs for communities of Q&A websites. The proposed method recommends QAs by considering the community members\' reputations, the push scores and collection time of QAs, the complementary relationships between QAs and their relevance to the communities. Experimental results show that the proposed method outperforms other conventional methods, providing a more effective manner to recommend QA documents to knowledge communities.
QA document recommendations for communities of question-answering websites
(2014-02-01)
Liu, Duen-Ren
;
Chen, Yu-Hsuan
;
Huang, Chun-Kai
;
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 rapid development of Internet and Web 2.0 technology, Question & Answering (Q&A) websites have become an essential knowledge-sharing platform. This platform provides knowledge community services where users with common interests or expertise can form a knowledge community. Community members can collect and share QA knowledge (documents) regarding their interests. However, due to the massive amount of QAs created every day, information overload can become a major problem. Consequently, a recommendation mechanism is needed to recommend QA documents for communities of Q&A websites. Existing studies did not investigate the recommendation mechanisms for knowledge collections in communities of Q&A Websites. Traditional recommendation methods use member importance as weight to consolidate individual profiles and generate group profiles, which in turn are used to filter out items of recommendation. However, they do not consider certain factors of the recommended items, such as the reputation of the community members and the complementary relationships between documents. In this work, we propose a novel method to recommend related QA documents for knowledge communities of Q&A websites. The proposed method recommends QA documents by considering factors such as the community members' reputation in collecting and answering QAs, the push scores and collection time of QAs, the complementary relationships between QAs and their relevance to the communities. This research evaluates and compares the proposed methods using an experimental dataset collected from Yahoo! Answers Taiwan website. Experimental results show that the proposed method outperforms other conventional methods, providing a more effective manner to recommend Q&A documents to knowledge communities. (C) 2013 Elsevier B.V. All rights reserved.
Variable consistency and variable precision models for dominance-based fuzzy rough set analysis of possibilistic information systems
(2013-08-01)
Fan, Tuan-Fang
;
Liau, Churn-Jung
;
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
The dominance-based fuzzy rough set approach (DFRSA) is a theoretical framework that can deal with multi-criteria decision analysis of possibilistic information systems. While a set of comprehensive decision rules can be induced from a possibilistic information system by using DFRSA, generation of several intuitively justified rules is sometimes blocked by objects that only partially satisfy the antecedents of the rules. In this paper, we use the variable consistency models and variable precision models of DFRSA to cope with the problem. The models admit rules that are not satisfied by all objects. It is only required that the proportion of objects satisfying the rules must be above a threshold called a consistency level or a precision level. In the presented models, the proportion of objects is represented as a relative cardinality of a fuzzy set with respect to another fuzzy set. We investigate three types of models based on different definitions of fuzzy cardinalities including Sigma-counts, possibilistic cardinalities, and probabilistic cardinalities; and the consistency levels or precision levels corresponding to the three types of models are, respectively, scalars, fuzzy numbers, and random variables.
Discovering role-based virtual knowledge flows for organizational knowledge support
(2013-04-01)
Liu, Duen-Ren
;
Lin, Chih-Wei
;
Chen, Hui-Fang
;
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 knowledge-intensive work environments, workers need task-relevant knowledge and documents to support the execution of tasks. A knowledge flow (KF) represents an individual's or group's knowledge-needs and referencing behavior of codified knowledge during the performance of organizational tasks. Through knowledge flows, organizations can provide workers with task-relevant knowledge to satisfy their knowledge-needs. In teamwork environments, knowledge workers with different roles and task functions usually have diverse knowledge-needs, but conventional KF models cannot satisfy such needs. In a previous work, we proposed a novel concept and theoretical model called Knowledge Flow View (KFV). Based on workers diverse knowledge-needs, the KFV model abstracts knowledge nodes of partial KFs and generates virtual knowledge nodes through a knowledge concept generalization procedure. However, the KFV model did not consider the diverse knowledge-needs of workers who play different roles in a team. Therefore, in this work, we propose a role-based KFV model that discovers role-based virtual knowledge flows to satisfy the knowledge-needs of different roles. First, we analyze the level of knowledge required by workers to fulfill various roles. Then, we develop role-based knowledge flow abstraction methods that generate appropriate virtual knowledge nodes to provide sufficient knowledge for each role. The proposed role-based ITV model enhances the efficiency of KF usage, as well as the effectiveness of knowledge sharing and knowledge support in organizations. (c) 2013 Elsevier B.V. All rights reserved.
Novel personal and group-based trust models in collaborative filtering for document recommendation
(2013-08-01)
Lai, Chin-Hui
;
Liu, Duen-Ren
;
Lin, Cai-Sin
;
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.
Enhancing Sales Forecasting by using Neuro Networks and the Popularity of Magazine Article Titles
(2012)
Omar, Hani A.
;
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
In this paper, we examine how the popularity information of magazines can be useful for sales forecasting. We propose a sales forecasting model based on Back Propagation Neural Network (BPNN) where the inputs are historical sales and the popularity indexes of magazine article titles. Our proposed model using the popularity of magazine article titles in the forecasting process can improve the accuracy of sales forecasting.

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