Automatic Recommendation of Web Pages for Online Users Using Web Usage Mining

Bhushan, Ravi; Nath, Rajender

doi:10.1109/iccs.2012.17

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…The main purpose for structure mining is to extract previously unknown relationships between Web pages. While this paper falls within the scope of structure web mining, most of the literature in this topic focus on recommendation systems and web personalization [3]. In [22] authors presented a comparative study of the navigation behavior of customers and non-customers to assess and improve the quality of a commercial web site; while in this work we aim to build a process model that shows the complete interactions of most users in the site that includes customer sessions.…”

Section: Background and Related Workmentioning

confidence: 99%

Business Process Mining from E-Commerce Web Logs

Poggi

Muthusamy²,

Carrera

et al. 2013

Lecture Notes in Computer Science

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Abstract. The dynamic nature of the Web and its increasing importance as an economic platform create the need of new methods and tools for business efficiency. Current Web analytic tools do not provide the necessary abstracted view of the underlying customer processes and critical paths of site visitor behavior. Such information can offer insights for businesses to react effectively and efficiently. We propose applying Business Process Management (BPM) methodologies to e-commerce Website logs, and present the challenges, results and potential benefits of such an approach.We use the Business Process Insight (BPI) platform, a collaborative process intelligence toolset that implements the discovery of looselycoupled processes, and includes novel process mining techniques suitable for the Web. Experiments are performed on custom click-stream logs from a large online travel and booking agency. We first compare Web clicks and BPM events, and then present a methodology to classify and transform URLs into events. We evaluate traditional and custom process mining algorithms to extract business models from real-life Web data. The resulting models present an abstracted view of the relation between pages, exit points, and critical paths taken by customers. Such models show important improvements and aid high-level decision making and optimization of e-commerce sites compared to current state-of-art Web analytics.

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Section: Background and Related Workmentioning

confidence: 99%

Business Process Mining from E-Commerce Web Logs

Poggi

Muthusamy²,

Carrera

et al. 2013

Lecture Notes in Computer Science

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“…Most of the existing research in web mining apply traditional data mining techniques [32], [33], [34], [35], [36]. The objectives of these works are to improve web site usages [32], [33] and to create recommender systems [34], [35]. Ontologies are also used to model the human behavior when navigating in information networks [36].…”

Section: Related Workmentioning

confidence: 99%

Process mining for recommender strategies support in news media

Epure

Ingvaldsen

Deneckère

et al. 2016

2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS)

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The strategic transition of media organizations to personalized information delivery has urged the need for richer methods to analyze the customers. Though useful in supporting the creation of recommender strategies, the current data mining techniques create complex models requiring often an understanding of techniques in order to interpret the results. This situation together with the recommender technologies deluge and the particularities of the news industry pose challenges to the news organization in making decisions about the most suitable strategy. Therefore, we propose process mining as a high-level, end-toend solution to provide insights into the consumers' behavior and content dynamics. Specifically, we explore if it allows news organizations to analyze independently and effectively their data in order to support them in defining recommender strategies. The solution was implemented in a case study with the third largest news provider in Norway and yielded preliminary positive results. To our knowledge, this is the first attempt to apply a process mining methodology and adapt the techniques to support media industry with the recommender strategies.

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“…Senkul and Salin (2012) investigated the effect of semantic information on the patterns generated for web usage mining in the form of frequent sequences. Bhushan and Nath (2012) suggested a paper for an automatic recommendatin of web pages for online users using web usage mining. In this paper, they implied a web recommendation approach which recommends user a list of pages based upon user's historic pattern.…”

Section: Related Workmentioning

confidence: 99%

Recommendation Engine Formation Using Depth First Search and Genetic Approach

Kanchana¹,

Sujatha²

2015

Journal of Computer Science

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Abstract:The requirement of online users in the website varies dynamically. The recommendation of web pages consisting of user expected information and data is performed by the online recommendation system. The recommendation engine must be selfadaptive and accurate. The existing algorithm uses Depth First Search (DFS) and bee's foraging approach to create navigation profiles by categorizing the current user activity. The prediction of navigations that are most expected to be visited by online users is also performed. In this study, the recommendation engine formation with optimized resource such as memory, CPU usage and minimum time consumption is proposed using DFS and Genetic Approach (GA). Here, initially the cluster formation is achieved using DFS approach. The method creates an eminent browsing pattern for each user using live session window. The performance of the approach is compared with the existing forager agent. The experimental results show that the proposed approach outperforms the existing methods in accomplishing accurate classification and anticipation of future navigation for the current online user.

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Automatic Recommendation of Web Pages for Online Users Using Web Usage Mining

Cited by 6 publications

References 8 publications

Business Process Mining from E-Commerce Web Logs

Business Process Mining from E-Commerce Web Logs

Process mining for recommender strategies support in news media

Recommendation Engine Formation Using Depth First Search and Genetic Approach

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