Browsing by Author "Liao, Yu-Shan"

Now showing 1 - 4 of 4

Advertisement recommendation based on personal interests and ad push fairness
(2019-01-01)
Liu, Duen-Ren
;
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.
Intelligent Power Resource Allocation by Context-Based Usage Mining
(2015)
Liao, Yu-Shan
;
Liao, Hsiu-Yu
;
Liu, Duen-Ren
;
Fan, Wen-Ting
;
Omar, Hani
The smart meter is an emerging appliance which can be used to collect and monitor the power consumption data on electrical devices. Users can monitor these devices in order to adjust their operations for energy saving results. To avoid wasting power, it is important to set a power usage constraint to effectively monitor and control users\' power consumption within a restricted power usage range. However, existing researches on smart-metering systems do not address this issue. In this study, a novel Intelligent Energy Saving (IES) system is proposed to effectively monitor and utilize the power usages of devices under the power consumption constraint. The IES system conducts context-based mining to discover device usage patterns and provide intelligent analysis of context-based usage behaviors, effective monitoring and utilization of power usages of devices for intelligent energy saving.
NEWS RECOMMENDATION BASED ON COLLABORATIVE SEMANTIC TOPIC MODELS AND RECOMMENDATION ADJUSTMENT
(2019-01-01)
Liao, Yu-Shan
;
Lu, Jun-Yi
;
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
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.
Online news recommendations based on topic modeling and online interest adjustment
(2019-09-09)
Liu, Duen-Ren
;
Liao, Yu-Shan
;
Lu, Jun-Yi
;
交大名義發表
;
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.