Applying an intelligent notification mechanism to blogging systems utilizing a genetic-based information retrieval approach
Introduction
In recent years, due to the prevalence of computers and the advancement of internet technologies, blogging applications have become more popular and have started to change the people’s daily lives, such as through e-business (Chen, Tsai, & Chan, 2008) and e-learning (Du and Wagner, 2007, Huang et al., 2008, Wang et al., 2008). A blog is a simple personal publishing platform which enables people to publish their thoughts and then to gather readers’ comments (Lindahl & Blount, 2003). The owner of a blog is called a blogger, who uses the post function to publish articles. Readers can use the comment function to express their opinions about articles on the blog. For example, a company can use the post news associated with free samples via their blog platform, and then consumers can use the comment function to discuss their experience of using the samples (Murugesan, 2007). The company can then improve the shortcomings of the samples via consumer feedback.
Although the comment function is usually used to obtain feedback from readers, some problems can emerge. For example, when the latest comments appear on a blog, the blogger does not know about them unless he or she browses the blog continuously. Therefore, a smart blogging system needs to automatically determine whether any new comments have appeared on the blog. Unfortunately, a blogger might not be interested in all new comments, and therefore, the blogging system has to offer an intelligent notification mechanism to filter for user-specified comments and then inform the blogger.
Many studies have investigated blogs (Chen et al., 2008, Du and Wagner, 2007, Huang et al., 2008, Kwai Fun and Wagner, 2008, Kuan et al., 2008, Lin et al., 2007, Lin et al., 2008, Thelwall and Hasler, 2007, Wang et al., 2008). However, most studies do not take the effect of the notification mechanism on blog comments into account. Previous studies directly used basic blog platforms for various applications, and thus, a blogger would need to spend a lot of time to frequently visit his or her blog, which is inconvenient in real-world applications. An intelligent notification mechanism is a very important issue that needs to be resolved in blogging platforms. To the best of our knowledge, the notification mechanism for blog comments has not been studied before.
In this paper, an intelligent notification mechanism is designed to provide highly accurate comment notification for bloggers. The idea is originated from the auto-reply service system which efficiently and automatically answers students’ questions (Hwang et al., 2008, Tseng and Hwang, 2007). We propose a Two-stage Intelligent Notification Mechanism (TINM) for blog comments. In the first stage, a genetic algorithm (Holland, 1975) is used to design a Genetic-based Information Retrieval Approach (GIRA) to retrieve the keyword interest value associated with the blogger’s keywords. By using GIRA, the TINM can retrieve the keyword interest value and dramatically reduce the computation cost. In the second stage, we use the results of GIRA (i.e., the keyword interest value) to evaluate the similarity between the new comment and a user-interested comment (i.e. user-specified comment), and determines whether the system needs to notify the blogger. By employing this new technology in blogging platforms, bloggers can easily monitor new interesting comments. For instance, companies could apply TINM to build smart business blogging systems for monitoring customer responses to specific products. Companies could can quickly obtain consumer feedback, and then rapidly reply to customer needs. In a learning environment, a teacher could build an educational blog with TINM so that students could get useful and meaningful comments posted from the teacher or other peers.
The rest of this paper is organized as follows. Section 2 reviews the related studies, which include blog research and genetic algorithms in information retrieval. In Section 3, we describe the parameters and define our problem. Section 4 presents the proposed Two-stage Intelligent Notification Mechanism (TINM). Section 5 shows the experiment results. Finally, a brief conclusion is given in Section 6.
Section snippets
Related studies
In this section, we give a brief introduction of blogs and the current research on blogs. We then introduce Genetic Algorithms (GA) and several studies that have applied GA in information retrieval. Finally, we present the differences between this study and previous research.
Theoretical foundations of TINM
The TINM problem is to understand whether a new comment is an interesting comment for the blogger; i.e., the TINM problem is to explore the blogger’s likes by applying information retrieval. Some information retrieval studies (Croft, 1987, Rijsbergen, 1986) suggested that significant improvements in retrieval performance will require techniques that, in some sense, “understand” the content of documents and queries (i.e., user’s demands) to infer probable relationships between documents and
Two-stage Intelligent Notification Mechanism (TINM)
In the first stage of TINM (hereafter named the offline stage), a training process is designed to retrieve the keyword interest value. In order to increase the training efficiency, a Genetic-based Information Retrieval Approach (GIRA) is designed to reduce the computation cost for retrieving the optimal keyword interest value. In the second stage of TINM (hereafter named the online stage), an intelligent notification mechanism is presented, which has two main concerns. (1) The results of
Experiments
Three experiments were conducted to evaluate the performance of the proposed approach. The first experiment evaluated the accuracy of keyword interest value optimization. The second experiment was conducted to observe whether the fitness value increases and converges as the generation number increases in various scenarios. In the third experiment, we compared the computation cost of optimization between GIRA and the enumeration approach. The system prototype designed for the experiment was
Conclusion
Blogs are critical web-based applications. However, previous blog research has not considered the effect of the comment notification mechanism. In this paper, we proposed TINM to improve the interaction of people in blogging applications. TINM can identify whether the comments correspond with users’ interests and notify them to view the comments.
To identify interesting comments, we designed GIRA to retrieve the optimal keyword interest value table (OKIV table). Interesting comments can be
Acknowledgements
The authors would like to thank the National Science Council of the Republic of China for financially supporting this research under Contract No. NSC 97-2511-S-006-001-MY3. The authors are grateful to the reviewers and the editor for their constructive comments and assistance in revising and polishing this paper.
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