Search results

1 – 10 of 10
To view the access options for this content please click here
Article
Publication date: 11 September 2019

Duen-Ren Liu, Yu-Shan Liao and Jun-Yi Lu

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…

Abstract

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.

Details

Industrial Management & Data Systems, vol. 119 no. 8
Type: Research Article
ISSN: 0263-5577

Keywords

To view the access options for this content please click here
Article
Publication date: 23 October 2018

Duen-Ren Liu, Yu-Shan Liao, Ya-Han Chung and Kuan-Yu Chen

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…

Abstract

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.

Details

Kybernetes, vol. 48 no. 8
Type: Research Article
ISSN: 0368-492X

Keywords

To view the access options for this content please click here
Article
Publication date: 9 January 2020

Duen-Ren Liu, Yun-Cheng Chou and Ciao-Ting Jian

Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits…

Abstract

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.

To view the access options for this content please click here
Article
Publication date: 12 February 2018

Duen-Ren Liu, Yun-Cheng Chou, Chi-Ching Chung and Hsiu-Yu Liao

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…

Abstract

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.

Details

Kybernetes, vol. 47 no. 3
Type: Research Article
ISSN: 0368-492X

Keywords

To view the access options for this content please click here
Article
Publication date: 9 September 2014

Duen-Ren Liu, Chuen-He Liou, Chi-Chieh Peng and Huai-Chun Chi

Social bookmarking is a system which allows users to share, organise, search and manage bookmarks of web resources. However, with the rapid growth in the production of…

Abstract

Purpose

Social bookmarking is a system which allows users to share, organise, search and manage bookmarks of web resources. However, with the rapid growth in the production of online documents, people are facing the problem of information overload. Social bookmarking web sites offer a solution to this by providing push counts, which are counts of users’ recommendations of articles, and thus indicate the popularity and interest thereof. In this way, users can use the push counts to find popular and interesting articles. A measure of popularity-based solely on push counts, however, cannot be considered a true reflection of popularity. The paper aims to discuss these issues.

Design/methodology/approach

In this paper, the authors propose to derive the degree of popularity of an article by considering the reputation of the users who push the article. Moreover, the authors propose a novel personalised blog article recommendation approach which combines reputation-based group popularity with content-based filtering (CBF), for the recommendation of popular blog articles which satisfy users’ personal preferences.

Findings

The experimental results show that the proposed approach outperforms conventional CBF, item-based and user-based collaborative filtering approaches. The proposed approach considering reputation-based group popularity scores on neighbouring articles indeed can improve the recommendation quality of traditional CBF method.

Originality/value

The recommendation approach modifies CBF method by considering the target user's group preferences, to overcome the limitation of CBF which arises from the recommending only items similar to those the user has previously liked. Users with similar article preferences (profiles) may form a group of users with similar interests. A group's preferences may also reflect an individual's preferences. The reputation-based group preferences of the target user's group can be used to complement the target user's preferences.

Details

Online Information Review, vol. 38 no. 6
Type: Research Article
ISSN: 1468-4527

Keywords

To view the access options for this content please click here
Article
Publication date: 25 January 2013

Duen‐Ren Liu, Wei‐Hsiao Chen and Po‐Huan Chiu

In recent years, readers have limited amounts of time to pick the right books to read from a market that is filled with similar types of books. Aiming to read only good…

Abstract

Purpose

In recent years, readers have limited amounts of time to pick the right books to read from a market that is filled with similar types of books. Aiming to read only good books, readers tend to check book reviews written by others. However, it is very difficult to find good book reviews. The aim of this paper is to present a book review recommendation system that collects reviews from heterogeneous sources on the Internet and performs quality judgments automatically. Users can then read the top‐ranked reviews suggested by this recommendation system.

Design/methodology/approach

In this paper, a book review recommendation system is constructed to collect, process, and judge the quality of book reviews from various heterogeneous sources. The quality measurement of book reviews uses review‐evaluation techniques. The prediction results were validated with a ranking list produced by experts.

Findings

The proposed system is effective and suitable for recommending quality book reviews from heterogeneous sources. The proposed quality measurement method is more effective than other more commonly used methods.

Originality/value

This paper is one of the first to apply review evaluation techniques to the process of book review recommendation. The proposed system can collect and recognize book reviews from different websites with various forms of presentation. This evaluation shows that the quality measurement method produces better results than do other methods, such as ranking by rating score or by the date that the review was posted. Those methods are primarily used by commercial websites.

Details

Internet Research, vol. 23 no. 1
Type: Research Article
ISSN: 1066-2243

Keywords

To view the access options for this content please click here
Article
Publication date: 1 July 2004

Duen‐Ren Liu and Chouyin Hsu

Many enterprises implement various business projects on the Internet in the global knowledge economy. The task of managing distributed and heterogeneous project knowledge…

Abstract

Many enterprises implement various business projects on the Internet in the global knowledge economy. The task of managing distributed and heterogeneous project knowledge is very important in increasing the knowledge assets of enterprises. Accordingly, this work presents a project‐based knowledge map system to properly organize project knowledge into topic maps, from which users can obtain in‐depth concepts to facilitate further project development. A two‐phase data mining approach involving the ISO/ISEC 13250 topic maps and Extensible Markup Language (XML) is used to establish the proposed system, which can determine knowledge patterns from previous projects and transform these patterns into a navigable knowledge map. The map can help users to locate required information and also offers subject‐related information easily and rapidly over the Internet.

Details

Internet Research, vol. 14 no. 3
Type: Research Article
ISSN: 1066-2243

Keywords

To view the access options for this content please click here
Article
Publication date: 3 October 2016

Saeedeh Hazratzadeh and Nima Jafari Navimipour

Expert Cloud as a new class of cloud systems enables its users to request and share the skill, knowledge and expertise of people by employing internet infrastructures and…

Abstract

Purpose

Expert Cloud as a new class of cloud systems enables its users to request and share the skill, knowledge and expertise of people by employing internet infrastructures and cloud concepts. Since offering the most appropriate expertise to the customer is one of the clear objectives in Expert Cloud, colleague recommendation is a necessary part of it. So, the purpose of this paper is to develop a colleague recommender system for the Expert Cloud using features matrices of colleagues.

Design/methodology/approach

The new method is described in two phases. In the first phase, all possible colleagues of the user are found through the filtering mechanism and next features of the user and possible colleagues are calculated and collected in matrices. Six potential features of colleagues including reputation, expertise, trust, agility, cost and field of study were proposed. In the second phase, the final score is calculated for every possible colleague and then top-k colleagues are extracted among users. The survey was conducted using a simulation in MATLAB Software. Data were collected from Expert Cloud website. The method was tested using evaluating metrics such as precision, accuracy, incorrect recommendation and runtime.

Findings

The results of this study indicate that considering more features of colleagues has a positive impact on increasing the precision and accuracy of recommending new colleagues. Also, the proposed method has a better result in reducing incorrect recommendation.

Originality/value

In this paper, the colleague recommendation issue in the Expert Cloud is pointed out and the solution approach is applied into the Expert Cloud website.

Details

Kybernetes, vol. 45 no. 9
Type: Research Article
ISSN: 0368-492X

Keywords

To view the access options for this content please click here
Article
Publication date: 1 December 2003

Eldon Y. Li and Xiande Zhao

An issue devoted to the Second International Conference on Electronic Business, in December 2002, in Taiwan. Included are six papers, taken from a total of 205 papers that…

Abstract

An issue devoted to the Second International Conference on Electronic Business, in December 2002, in Taiwan. Included are six papers, taken from a total of 205 papers that were originally submitted, accepted and included in the conference proceedings.

Details

International Journal of Service Industry Management, vol. 14 no. 5
Type: Research Article
ISSN: 0956-4233

Keywords

To view the access options for this content please click here
Article
Publication date: 15 March 2021

Yung-Ting Chuang and Yi-Hsi Chen

The purpose of this paper is to apply social network analysis (SNA) to study faculty research productivity, to identify key leaders, to study publication keywords and…

Abstract

Purpose

The purpose of this paper is to apply social network analysis (SNA) to study faculty research productivity, to identify key leaders, to study publication keywords and research areas and to visualize international collaboration patterns and analyze collaboration research fields from all Management Information System (MIS) departments in Taiwan from 1982 to 2015.

Design/methodology/approach

The authors first retrieved results encompassing about 1,766 MIS professors and their publication records between 1982 and 2015 from the Ministry of Science and Technology of Taiwan (MOST) website. Next, the authors merged these publication records with the records obtained from the Web of Science, Google Scholar, IEEE Xplore, ScienceDirect, Airiti Library and Springer Link databases. The authors further applied six network centrality equations, leadership index, exponential weighted moving average (EWMA), contribution value and k-means clustering algorithms to analyze the collaboration patterns, research productivity and publication patterns. Finally, the authors applied D3.js to visualize the faculty members' international collaborations from all MIS departments in Taiwan.

Findings

The authors have first identified important scholars or leaders in the network. The authors also see that most MIS scholars in Taiwan tend to publish their papers in the journals such as Decision Support Systems and Information and Management. The authors have further figured out the significant scholars who have actively collaborated with academics in other countries. Furthermore, the authors have recognized the universities that have frequent collaboration with other international universities. The United States, China, Canada and the United Kingdom are the countries that have the highest numbers of collaborations with Taiwanese academics. Lastly, the keywords model, system and algorithm were the most common terms used in recent years.

Originality/value

This study applied SNA to visualize international research collaboration patterns and has revealed some salient characteristics of international cooperation trends and patterns, leadership networks and influences and research productivity for faculty in Information Management departments in Taiwan from 1982 to 2015. In addition, the authors have discovered the most common keywords used in recent years.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

Keywords

1 – 10 of 10