Information Sciences 305 (2015) 302–319

Contents lists available at ScienceDirect

Information Sciences journal homepage: www.elsevier.com/locate/ins

Recommending blog articles based on popular event trend analysis Duen-Ren Liu a,⇑, Hani Omar a, Chuen-He Liou b, Huai-Chun Chi a, Cheng-Ho Hsu a a b

Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan Center of General Education, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan

a r t i c l e

i n f o

Article history: Received 6 June 2013 Received in revised form 8 December 2014 Accepted 1 February 2015 Available online 7 February 2015 Keywords: Blogosphere Popular event-based recommendation Trend analysis Google Insights Content-based filtering Item-based collaborative filtering

a b s t r a c t Web 2.0 has become a popular social media on the Internet due to the fast evolution of Internet technologies, as well as increasing resources and users. Among the applications of Web 2.0, blogospheres are a new Internet social media for users to express their preferences and personal feelings. Most of the people tend to receive the newest information and articles related to popular issues. However, with the rapidly increasing number of active writers and viewers, it is hard for people to discover useful information that is beneficial or interesting to them. Accordingly, it is necessary to develop a recommendation approach that takes the emerging or popular events into consideration. In this work, we propose a novel event-based recommendation approach, which combines the event trend analysis and personal preference to recommend blog articles of popular events that suit user interests. We analyze blog articles to identify popular events, and then derive the popularity degrees of events based on blog-based popularity trend analysis and Google Insights-based popularity trend analysis. Our approach derives users’ personalized preferences on target articles of popular events by considering user interests (article-push records) and the predicted popularity degree of the events. Our recommendation methods improve recommendation accuracy by enhancing content-based filtering (CBF) and item-based collaborative filtering (ICF) with the event-based preference analysis. Our experiment result demonstrates that the proposed approach can effectively recommend users’ desired blog articles with respect to event popularity and personal interests. Ó 2015 Elsevier Inc. All rights reserved.

1. Introduction Web 2.0 has become a popular social media on the Internet due to the fast evolution of Internet technologies, as well as increased resources and users. Web 2.0 is a web application where people can collaborate and share information which has led to the creation of new business models of software [41]. Among the applications of Web 2.0, blogospheres are a new Internet social media for users to express their preferences and personal feelings. In fact, the number of blogospheres has been rising rapidly in recent years; people prefer the blogospheres because the free platform provided by blogospheres, allows people to share the latest information and exchange opinions easily without technical constraints, instead of just retrieving information passively. Currently, blogospheres have become an indispensable information exchange platform which enables users to publish articles about their daily life or emerging news. In addition, there are several emerging events ⇑ Corresponding author. E-mail address: [email protected] (D.-R. Liu). http://dx.doi.org/10.1016/j.ins.2015.02.003 0020-0255/Ó 2015 Elsevier Inc. All rights reserved.

Hindawi Publishing Corporation Computational Intelligence and Neuroscience Volume 2016, Article ID 9656453, 9 pages http://dx.doi.org/10.1155/2016/9656453

Research Article A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles Hani Omar,1,2 Van Hai Hoang,3 and Duen-Ren Liu1 1

Institute of Information Management, National Chiao Tung University, Hsinchu 300, Taiwan Computational Intelligence Technology Center, Industrial Technology Research Institute, Chutung, Hsinchu 310, Taiwan 3 The University of Danang, Campus in Kon Tum, No. 129 Phan Dinh Phung Street, Kon Tum 580000, Vietnam 2

Correspondence should be addressed to Duen-Ren Liu; [email protected] Received 29 December 2015; Revised 17 April 2016; Accepted 3 May 2016 Academic Editor: Saeid Sanei Copyright © 2016 Hani Omar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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.

1. Introduction Forecasting is an important part of many aspects of our lives, and sales forecasting plays a major role for enterprises in making business plans more accurate and gaining competitive advantage. Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in any industry and business. Convenience stores allow people to buy things at anytime and anywhere. They have a huge market in Taiwan and many publishing providers (e.g., magazine business) want to cooperate with them. Magazine businesses need plans for enhancing sales, distribution, storage space, and high quality predictions. Accurate sales forecasting can reduce inventory costs and shortages. It would increase profits for the business by reducing wasted resources and allow planning for appropriate future production. Most studies have depended on historical sales data for forecasting sales, but the sales of magazines are also affected by the contents of the magazines. The contents are

represented by the stories (articles) and their titles. Popular content can often boost sales, so it is an important field to consider when forecasting sales. For example, a magazine reporting celebrity gossip, for example, Faye Wong’s extramarital affair, will attract readers who like her and want to know about her to buy the magazine. Thus, magazine sales will increase due to the popularity of the magazine contents. Before customers make decisions, they usually search for a product via the Internet in websites and blogs. Thus, search indexes of product data received via the Internet can be useful in deriving the popularity of products for building prediction models. Search terms related to the products can be decided manually. However, the terms manually decided may be limited when approximating consumer preferences. 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. The derived popularity denotes consumer interests in the contents of the magazines.

2012 Sixth International Conference on Genetic and Evolutionary Computing

Enhancing Sales Forecasting by using Neuro Networks and the Popularity of Magazine Article Titles Hani A. Omar, Duen-Ren Liu Institute of Information Management National Chiao Tung University, Hsinchu 300, Taiwan [email protected]; [email protected]

Abstract—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. Keywords – Forecasting, Neural-Network, Pupularity, Google Search engine. I.

allows users to submit links to news, images, and videos that are interest to the site’s general audience [14]. Moreover, search data have the potential to describe user interests in a variety of economic activities in real time [1]. While other research shows the observation that search counts are generally a prediction of consumer activities, such as purchasing music in the future [3]. These researches and the need to forecast are our motivation to use Google Search Engine for measuring the popularity of title words. The search engine results of title words of magazine articles reflect the readers’ interests and are important indicators of the sales. Using non-linear historical data of sales and the popularity of title words in our proposed model can contribute to improve the forecasting performance. Our experiment evaluation shows that our proposed model using the popularity of magazine article titles outperforms traditional methods. The rest of this paper is organized as follows. Section II presents a short survey of existing literatures on Neural Networks and Double Exponential Smoothing methods. Section III presents the proposed model for sales forecasting. Experimental evaluation is reported in section IV. The conclusions are summarized in section V.

INTRODUCTION

Sales forecasting is important for enterprises to make business plans and gain competitive advantages. The sales of magazines are usually affected by the contents of magazines. Popular contents can often boost the sales. It is interesting to estimate the effect of the magazines’ popularity on sales forecasting by analyzing the contents of magazines and measuring their popularity using search engines. The objective of this research is to investigate how to utilize the popularity information of magazines derived from Google Search engine to improve sales forecasting. Several contributions have been made in the field of forecasting [9]. Traditional time series methods are confined to the assumption of linearity, but some data are nonlinear. In order to overcome this limitation of traditional methods, many researchers use soft computing techniques such as fuzzy logic, neural network, fuzzy neural network, evolutionary algorithm etc [11]. If there are nonlinearities in the process being modeled, then a method which can account for these nonlinearities should produce superior forecast. Neural Networks are reported to be such a method [4]. Neural networks are also more noise tolerant, having the ability to learn complex systems with incomplete and corrupted data. In addition, they are more flexible, having the capability to learn dynamic systems through a retraining process using new data patterns [10]. In this paper, we proposed 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. We utilize the popularity of celebrity words appeared in article titles to predict the sales of magazines. Some tools and web sites provide functions to estimate the popularity of keywords based on search counts or web-page counts. If popularity count is tied directly to ad revenue (such as with ads shown with YouTube videos), revenue might fairly accurately be estimated ahead of time if all parties know how many views the video is likely to attract. Meanwhile, Digg 978-0-7695-4763-3/12 $26.00 © 2012 IEEE DOI 10.1109/ICGEC.2012.87

II.

THEORETICAL BACKGROUND

Several contributions have been made in the field of forecasting. Statistical forecasting methods outperform the simplistic models in terms of forecasting accuracy [6]. However, there are two major drawbacks of these methods. First, for each problem, an individual statistical model has to be chosen that makes some assumptions about underlying trends. Second, the power of deterministic data analysis needs to be exploited for single time series with some hidden regularity [15]. Artificial Neural Network has been adopted to replace traditional methods because of better performance, adaptive capability and so on [5]. A. Introduction to Artificial Neural Network An artificial neural network (ANN), often called a "neural network" [12]. ANN is an information processing system that has been developed as generalization of mathematical models of human neural biology. ANN is composed of nodes or units connected by directed links. Each link has a numeric weight [8]. ANN adjusts the weights such that the predicted values and the real values are as close as possible [13]. There are more than one type of ANN depending on the way of learning and adjusting the weights for hidden layer(s). Back 581 577

2015 IIAI 4th International Congress on Advanced Applied Informatics

Intelligent Power Resource Allocation by ContextBased Usage Mining Yu-Shan Liao, Hsiu-Yu Liao, Duen-Ren Liu, Wen-Ting Fan, Hani Omar Institute of Information Management National Chiao Tung University, Taiwan Corresponding author: Professor Duen-Ren Liu, [email protected]

People may unconsciously waste power by consuming extra power for unnecessary devices. Setting reasonable power consumption constraints is the key action to counter wasted energy. Under the power usage constraint, users may face power shortages in operating devices when the power usages are not well managed. Effective utilization should ensure that there is available power for the operation of high-priority devices and adjust device operations dynamically for energy saving results to prevent possible shortages of power for device operations. In past studies, most researches focused on developing smartmetering systems for collecting devices’ power consumption data [5, 6, 10, 20, 21, 24]. The existing smart-metering systems can provide statistical data on power usage to users, which may be displayed by using an In-Home Display (IHD) device [6, 13, 24]. Based on this information, users can monitor devices’ power usages and make judgments to adjust the devices’ operations, thereby achieving energy saving. To achieve effective utilization of power usage, it is important to dynamically monitor and allocate required power usages for probable device operations based on the predictions of users’ device usage behaviors. Although existing studies have analyzed the usage data collected from smart-meters to derive statistical information on device usages, they did not conduct further analyses on users’ device usage records to predict users’ device usage behavior for realizing the effective allocation and utilization of power usage resources.

Abstract—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. Keywords—Energy saving; Smart meter; Power consumption; Data Mining; Power resource allocation

I. INTRODUCTION The increasing global energy demands will undoubtedly cause resource shortages. Moreover, carbon dioxide emissions and global warming are becoming critical issues. To help solve the energy problem and global warming, it is important for users to monitor their power consumption [1-3]. The smart meter is an emerging appliance which can be used to collect and monitor data on devices’ consumption of power [4-6]. Smart meters are also attractive for household applications, to adjust their operations to achieve optimal energy saving [7-10]. Emerging research efforts are being devoted to home intelligence to support people’s daily activities with minimal energy use [11, 12]. An energy-aware smart home can be developed by integrating energy efficiency features into its infrastructure, which monitor and control the environment [13]. Moreover, reducing energy consumption is also important in wireless sensor networks (WSN) for future Internet and ubiquitous smart environments [14-16]. Several researches introduce the trends and advantages of smart meters [1, 17-19]. Some studies focus on the architectures [6, 13, 18], communication between smart meters [20] and the formulation of fundamental standards [8, 21-23]. Some researchers are also applying smart meters to energy saving [5, 10]. Their researches mainly build display interfaces connected to a smart meter to provide the devices’ power consumption usage according to user requests. 978-1-4799-9958-3/15 $31.00 © 2015 IEEE DOI 10.1109/IIAI-AAI.2015.165

In this work, we propose an Intelligent Energy Saving (IES) system to effectively monitor and utilize the power usages of devices under a power consumption constraint. The power consumption constraints for users are determined based on the analysis of users’ historical device usage records. The IES system employs “users’ usage behavior” to determine the most suitable usage of devices, in order to provide users with smarter electronic consumption. IES system can achieve effective utilization of power by dynamically monitoring and adjusting device operations, based on the predictions of users’ (preference) device usage behaviors. We propose conducting context-based mining on users’ historical device usage records to predict users’ device usage behavior in certain contexts. Once predicting users’ device usage behaviors is feasible, the system can then allocate, in advance, the required power usages to probable device operations. Finally, a prototype 546