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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Author: search.filters.author.Liu, Duen-Ren

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Now showing 1 - 10 of 75
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    NEWS RECOMMENDATION BASED ON COLLABORATIVE SEMANTIC TOPIC MODELS AND RECOMMENDATION ADJUSTMENT
    (2019-01-01)
    Liao, Yu-Shan
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    Lu, Jun-Yi
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    Liu, Duen-Ren
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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
    Providing news recommendations is an important trend for online news websites to attract more users and create more benefits. In this research, we propose a novel recommendation approach for recommending news articles. We propose A Collaborative Semantic Topic Model and an ensemble model to predict user preferences based on combining Matrix Factorization with articles' semantic latent topics derived from word embedding and Latent Dirichlet Allocation. The proposed ensemble model is further integrated with a recommendation adjustment mechanism to adjust users' online recommendation lists. We evaluate the proposed approach via offline experiments and online evaluation on a real news website. The experimental result demonstrates that our proposed approach can improve the recommendation quality of recommending news articles.
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    Air pollution forecasting based on attention-based LSTM neural network and ensemble learning
    (1970-01-01)
    Liu, Duen-Ren
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    Lee, Shin-Jye
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    Huang, Yang
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    Chiu, Chien-Ju
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    科技管理研究所
    With air pollution having become a global concern, scientists are committed to working on its amelioration. In the field of air pollution prediction, there have been good results in experimental research so far, but few studies have integrated weather forecast information and the properties of air pollution drift. In this work, we propose a novel wind-sensitive attention mechanism with a long short-term memory (LSTM) neural network model to predict the air pollution - PM2.5 concentrations by considering the influence of wind direction and speed on the changes of spatial-temporal PM2.5 concentrations in neighbouring areas. Preliminary predictions for PM2.5 are then made by an LSTM neural network regarding neighbouring pollution; these predictions are "paid attention to" and we finally apply an ensemble learning method based on eXtreme Gradient Boosting (XGBoost) to combine the preliminary predictions with weather forecasting to make second phase predictions of PM2.5. The experiment is conducted using PM2.5 data and weather forecast data. Our results illustrate that the proposed method is superior to other methods in predicting PM2.5 concentrations, including multi-layer perceptron, support vector regression, LSTM neural network, and extreme gradient boosting algorithm.
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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
    ;
    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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    Online Recommendation based on Collaborative Topic Modeling and Item Diversity
    (2018-01-01)
    Liu, Duen-Ren
    ;
    Chou, Yun-Cheng
    ;
    Jian, Ciao-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
    Online news websites provide diverse article topics, such as fashion news, entertainment and movie articles to attract more users and create more benefits. Analyzing users' browsing behaviors and preferences to provide online recommendations is an important trend for online news websites. In this research, we propose a novel online recommendation method for recommending movie articles to users when they are browsing the news. Specifically, association rule mining is conducted on user browsing news and movies to find the latent associations between news and movies. A novel online recommendation approach is proposed based on Latent Dirichlet Allocation, enhanced Collaborative Topic Modeling and the diversity of recommendations. We evaluate the proposed approach via an online evaluation on a real news website. The online evaluation results show that our proposed approach can enhance the click-through rate for recommending movie articles and alleviate the cold-start issue.
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    Advertisement recommendation based on personal interests and ad push fairness
    (2019-01-01)
    Liu, Duen-Ren
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    Liao, Yu-Shan
    ;
    Chung, Ya-Han
    ;
    Chen, Kuan-Yu
    ;
    資訊管理與財務金融系 註：原資管所+財金所
    Purpose - Online advertisement brings huge revenue to many websites. There are many types of online advertisement; this paper aims to focus on the online banner ads which are usually placed in a particular news website. The investigated news website adopts a pay-per-ad payment model, where the advertisers are charged when they rent a banner from the website during a particular period. In this payment model, the website needs to ensure that the ad pushed frequency of each ad on the banner is similar. Under such advertisement push rules, an ad-recommendation mechanism considering ad push fairness is required. Design/methodology/approach - The authors proposed a novel ad recommendation method that considers both ad-push fairness and personal interests. The authors take every ad's exposure time into consideration and investigate users' three different usage experiences in the website to identify the main factors affecting the interests of users. Online ad recommendation is conducted on the investigated news website. Findings - The results of the experiments show that the proposed approach performs better than the traditional approach. This method can not only enhance the average click rate of all ads in the website but also ensure reasonable fairness of exposure frequency of each ad. The online experiment results demonstrate the effectiveness of this approach. Originality/value - Existing researches had not considered both the advertisement recommendation and ad-push fairness together. With the proposed novel ad recommendation model, the authors can improve the ad click-through rate of ads with reasonable push fairness. The website provider can thereby increase the commercial value of advertising and user satisfaction.
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    Online news recommendations based on topic modeling and online interest adjustment
    (2019-09-09)
    Liu, Duen-Ren
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    Liao, Yu-Shan
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    Lu, Jun-Yi
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    交大名義發表
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    National Chiao Tung University
    Purpose Providing online news recommendations to users has become an important trend for online media platforms, enabling them to attract more users. The purpose of this paper is to propose an online news recommendation system for recommending news articles to users when browsing news on online media platforms. Design/methodology/approach A Collaborative Semantic Topic Modeling (CSTM) method and an ensemble model (EM) are proposed to predict user preferences based on the combination of matrix factorization with articles' semantic latent topics derived from word embedding and latent topic modeling. The proposed EM further integrates an online interest adjustment (OIA) mechanism to adjust users' online recommendation lists based on their current news browsing. Findings This study evaluated the proposed approach using offline experiments, as well as an online evaluation on an existing online media platform. The evaluation shows that the proposed method can improve the recommendation quality and achieve better performance than other recommendation methods can. The online evaluation also shows that integrating the proposed method with OIA can improve the click-through rate for online news recommendation. Originality/value The novel CSTM and EM combined with OIA are proposed for news recommendation. The proposed novel recommendation system can improve the click-through rate of online news recommendations, thus increasing online media platforms' commercial value.
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    Churn prediction and social neighbour influences for different types of user groups in virtual worlds
    (2019-06-01)
    Liu, Duen-Ren
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    Liao, Hsiu-Yu
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    Chen, Kuan-Yu
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    Chiu, Yi-Ling
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    資訊管理與財務金融系 註：原資管所+財金所
    In virtual worlds (VWs), users have more VW games alternatives, whereas VW companies consequently suffer from high customer turnover rate and low customer loyalty. Therefore, building a churn prediction model to facilitate subsequent churn management and customer retention is important. The churn behaviours and the impact of social neighbour influences to customer churn may be different for different types of users. Accordingly, we segment users into stable, unstable, and solitary user groups according to their social contact behaviours in VWs. Novel segmentation-based churn prediction approaches are proposed for churn prediction in VWs by building prediction models for each type of user groups and considering the effect of social neighbour influences for different user groups. The proposed approaches are evaluated by conducting experiments with a dataset collected from a VW platform. The experimental results show different churn prediction performances under different user groups. The segmentation-based churn prediction approaches perform better than do general approaches without considering user groups. Moreover, the results also reveal that social neighbour influences have a positive impact on stable and unstable users. The proposed work contributes to investigating the social neighbour influences on churn prediction for different types of user groups in VWs.
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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
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    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.
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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
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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
    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.