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Now showing 1 - 6 of 6
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    Integrating collaborative topic modeling and diversity for movie recommendations during news browsing
    (1970-01-01)
    Liu, Duen-Ren
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    Chou, Yun-Cheng
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    Jian, Ciao-Ting
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    資訊管理與財務金融系 註：原資管所+財金所
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    Department of Information Management and Finance
    Purpose Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users' satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles. Design/methodology/approach Association rule mining is applied to users' news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website. Findings The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users' online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance. Originality/value Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user's online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.
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    An Online Activity Recommendation Approach based on the Dynamic Adjustment of Recommendation Lists
    (2017-01-01)
    Liu, Duen-Ren
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    Chen, Kuan-Yu
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    Chou, Yun-Cheng
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    Lee, Jia-Huei
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    資訊管理與財務金融系 註：原資管所+財金所
    This research investigates an online recommendation method for new types of online news websites. Cross-domain analysis on user browsing news and the attending activities is conducted to predict user preferences on activities based on non-negative Matrix Factorization (NMF) and Latent Dirichlet Allocation (LDA) topic model. A novel approach is proposed for the dynamic adjustment of recommendation lists in order to tackle the issue of limited recommendation layouts. The existing studies have not addressed this issue. The proposed approach is implemented on an online news website and evaluated for online recommendations. The experiment results demonstrate that our method can predict user preferences on recommended activities and enhance the effectiveness of recommendations.
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    Online recommendations based on dynamic adjustment of recommendation lists
    (2018-12-01)
    Liu, Duen-Ren
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    Chen, Kuan-Yu
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    Chou, Yun-Cheng
    ;
    Lee, Jia-Huei
    ;
    資訊管理與財務金融系 註：原資管所+財金所
    The flourishing of the Internet has increasingly promoted the rise of new types of online news websites with e-commerce portals. Online news websites provide specific information on such topics as lifestyle, fashion news, and a variety of other activities. The provision of online recommendation of activities associated with online news websites has the potential to attract more users and create more benefits. Such online recommendations represent an important online trend. Furthermore, dynamically adjusting recommendation lists to increase users' click-through rates is important for limited online recommendation layouts; however, existing studies have not addressed this online recommendation issue. This research proposes a novel approach for the dynamic adjustment of recommendation lists to tackle the issue of limited recommendation layouts, and then develops novel online recommendation methods. This research designs novel methods based on non-negative matrix factorization (NMF) and latent Dirichlet allocation (LDA) to predict user preferences for activities, using analysis of browsing news and attending activities. We propose a novel online activity recommendation approach, taking into consideration the interest scores and push scores, for dynamically adjusting the recommendation list. The Most Frequently Pushed (MFP) strategy gives priority to replacing the most frequently pushed activity, while the Not Frequently Clicked (NFC) strategy gives priority to replacing the not frequently clicked activity. We implement our proposed approach on an online news website and evaluate its online recommendation performance. The results of our experiment demonstrate that our proposed approach can enhance the effectiveness of recommendations. (C) 2018 Elsevier B.V. All rights reserved.
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    Recommender system based on social influence and the virtual house bandwagon effect in virtual worlds
    (2018-01-01)
    Liu, Duen-Ren
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    Chou, Yun-Cheng
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    Chung, Chi-Ching
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    Liao, Hsiu-Yu
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    資訊管理與財務金融系 註：原資管所+財金所
    Purpose Due to the rapidly increasing volume of users and products in virtual worlds, recommender systems are an important feature in virtual worlds; they can help solve information overload problems. Virtual world users are able to perform several actions that promote the enjoyment of their virtual life, including interacting with others, visiting virtual houses and shopping for virtual products. This study aims to concentrate on the following two important factors: the social neighbors' influences and the virtual house bandwagon phenomenon, which affects users' preferences during their virtual house visits and purchasing processes. Design/methodology/approach The authors determine social influence by considering the interactions between the target user and social circle neighbors. The degree of influence of the virtual house bandwagon effect is derived by analyzing the preferences of the virtual house hosts who have been visited by target users during their successive visits. A novel hybrid recommendation method is proposed herein to predict users' preferences by combining the analyses of both factors. Findings The recommendation performance of the proposed method is evaluated by conducting experiments with a data set collected from a virtual world platform. The experimental results show that the proposed method outperforms the conventional recommendation methods, and they also exhibit the effectiveness of considering both the social influence and the virtual house bandwagon effect for making effective recommendations. Originality/value Existing studies on recommendation methods did not investigate the virtual house bandwagon effects that are unique to the virtual worlds. The novel idea of the virtual house bandwagon effect is proposed and analyzed for predicting users' preferences. Moreover, a novel hybrid recommendation approach is proposed herein for generating virtual product recommendations. The proposed approach is able to improve the accuracy of preference predictions and enhance the innovative value of recommender systems for virtual worlds.
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    Large-scale recommender system with compact latent factor model
    (2016-12-01)
    Liu, Chien-Liang
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    Wu, Xuan-Wei
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    資訊工程學系
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    工業工程與管理學系
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    Department of Computer Science
    This work devises a factorization model called compact latent factor model, in which we propose a compact representation to consider query, user and item in the model. The blend of information retrieval and collaborative filtering is a typical setting in many applications. The proposed model can incorporate various features into the model, and this work demonstrates that the proposed model can incorporate context-aware and content-based features to handle context-aware recommendation and cold-start problems, respectively. Besides recommendation accuracy, a critical problem concerning the computational cost emerges in practical situations. To tackle this problem, this work uses a buffer update scheme to allow the proposed model to process data incrementally, and provide a means to use historical data instances. Meanwhile, we use stochastic gradient descent algorithm along with sampling technique to optimize ranking loss, giving a competitive performance while considering scalability and deployment issues. The experimental results indicate that the proposed algorithm outperforms other alternatives on four datasets. (C) 2016 Elsevier Ltd. All rights reserved.
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    An Intelligent Recommender System based on Short-term Risk Prediction for Heart Disease Patients
    (2015)
    Lafta, Raid
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    Zhang, Ji
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    Tao, Xiaohui
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    Li, Yan
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    Tseng, Vincent S.
    In this paper, an intelligent recommender system is developed, which uses an innovative time series prediction algorithm to provide recommendations to heart disease patients in the tele-health environment. Based on analytics of each patient\'s medical tests in records, the system provides the patient with decision support for necessity of medical tests. The experimental results show that the proposed system yields satisfactory accuracy in recommendations. The system also offers a promising way for saving the workload for patients and healthcare practitioners in conducting daily medical tests. The research will help reduce the workload and cost in healthcare and help the healthcare industry transform from the traditional scenario to more a personalized paradigm in a tele-health environment.