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With the 2026 enrollment cliff ever approaching, it is important for higher education institutions to ensure their public facing websites take advantage of technologies that improve their performance and user satisfaction. Web usage mining, the process of analyzing the usage patterns of website users to gain insights into the users' needs, has been researched extensively in both industry and higher education. Higher education has successfully used web usage mining to analyze e-learning platforms and other student focused applications to improve learning outcomes, however, research about the adoption of web usage mining technologies in higher education public facing websites is lacking. This is surprising as these websites are typically used for recruitment/marketing purposes. This study addresses the lack of research by working to answer the research question, what factors influence the adoption of web usage mining technologies in higher education websites? Combining the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model and Technology Organization Environment (TOE) framework, a survey instrument is created with the data collected used to both validate the model and answer the research question. [ABSTRACT FROM AUTHOR]

Data collection and data preprocessing are crucial stages in web usage mining, mainly because of the unstructured, diverse, and noisy nature of log data. During data collection, log file datasets are loaded and merged. Effective and comprehensive data preprocessing plays a vital role in ensuring the efficiency and scalability of algorithms used in the pattern discovery phase of web usage mining. This work aims to address these phases by introducing two innovative approaches. The first approach focuses on determining the device used for accessing the web, distinguishing between computers and mobile devices. The second approach aims to determine user sessions and complete paths by utilizing the referrer URL. The entire preprocessing pipeline has been implemented using the C# programming language, and the source code is available on GitHub at the following link: https://github.com/Mohammed91/Web-Usage-Mining.

The significance of the web and the crucial role of web archives in its preservation highlight the necessity of understanding how users, both human and robot, access web archive content, and how best to satisfy this disparate needs of both types of users. To identify robots and humans in web archives and analyze their respective access patterns, we used the Internet Archive's (IA) Wayback Machine access logs from 2012, 2015, and 2019, as well as Arquivo.pt's (Portuguese Web Archive) access logs from 2019. We identified user sessions in the access logs and classified those sessions as human or robot based on their browsing behavior. To better understand how users navigate through the web archives, we evaluated these sessions to discover user access patterns. Based on the two archives and between the three years of IA access logs (2012 vs. 2015 vs. 2019), we present a comparison of detected robots vs. humans and their user access patterns and temporal preferences. The total number of robots detected in IA 2012 (91% of requests) and IA 2015 (88% of requests) is greater than in IA 2019 (70% of requests). Robots account for 98% of requests in Arquivo.pt (2019). We found that the robots are almost entirely limited to "Dip" and "Skim" access patterns in IA 2012 and 2015, but exhibit all the patterns and their combinations in IA 2019. Both humans and robots show a preference for web pages archived in the near past. [ABSTRACT FROM AUTHOR]

Web recommender systems provide the most appropriate recommendations by analyzing user's navigation behavior. This recommender system can be considered in different cases, such as e-commerce, search engines, etc. The aim of the proposed approach is to create users' profiles and find their common navigation patterns implicitly. The web log file is utilized to analyze browsing history and discover users' navigation models. This research used K-means for the clustering and a neural network is designed for classification, along with the recommended Markov model. The novelty of this research is considering a threshold for the proposed Markov model. The efficiency (accuracy and precision) of the proposed method is confirmed in a test case. [ABSTRACT FROM AUTHOR]

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User engagement is a part of user experience that measures how attracted the users are in using a certain product or services. The level of user attention while navigating a website is a key factor in determining its effectiveness. Web usage mining can be performed on the weblog of a website to extract user navigational behavior and gain valuable insights into the useful activities of the users. The user navigational behavior can then be further evaluated to measure the user engagement of the website. This paper presents a new framework that uses user navigational behavior extracted from web usage mining to measure the engagement level of the users on the website. In this proposed framework, web session clustering is first performed on the pre-processed weblog to group similar user access patterns from web sessions. After that, four proposed engagement metrics, which are the hourly activeness, the hourly traffic, the daily activeness, and the daily traffic are calculated for each cluster to determine the engagement level of the users on the website. According to the engagement level determined by the user engagement metrics, an attention score is formulated to show how attracted the users in the session cluster are to the website. In this paper, two weblogs from different websites are used to measure their user engagement with the Cluster-N-Engage framework. The framework shows that attention score can assess the website’s effectiveness in achieving its objectives.

Data preparation is a vital step in the web usage mining process since it provides structured data for the subsequent stages. Hence, it is necessary to convert raw server logs into user sessions to generate structured data for pattern discovery phase. In recent decade, popular websites' server log production has risen to many terabytes to petabytes each day. As a result, server logs possess big data issues such as storage and processing. This study focuses on initial phases of web usage mining process such as data cleaning, user identification, and session identification. These phases are classified as data-intensive processes and deemed-computation intensive. In the last decade, MapReduce emerges as one of the best parallel programming frameworks for data-intensive applications. An efficient MapReduce-based data pre-processing algorithm, i.e. IRPDP_HT2, is proposed in this study. Previous parallel data pre-processing algorithms either include partial phases or lack with efficient robot detection approaches. IRPDP_HT2 algorithm uses a variety of efficient heuristics in all three phases of data pre-processing to identify both ethical and unethical robots. The suggested IRPDP_HT2 approach is found to be effective and scalable for larger datasets after various experiments on a cluster of nodes. The effectiveness of suggested heuristics is also examined during session identification phase. Three variants of IRPDP_HT2 such as PDP_HT2, IPDP_HT2, and RPDP_HT2 are also developed and tested. Impact of robots' requests and internal dummy connections' requests on session count by IRPDP_HT2 algorithm is 45.81% which is more than in PDP_HT2, IPDP_HT2, and RPDP_HT2 algorithms. Further speed-up and size-up are also analysed to demonstrate scalability of algorithm. In the presence of larger datasets, the algorithm's running time falls, while the number of data nodes grows. The size-up of IRPDP_HT2 demonstrates that even after doubling the input data, the algorithm's running time does not grow in that ratio for the fixed number of data nodes. [ABSTRACT FROM AUTHOR]

In this study, it is aimed to compare the learning management system (LMS) usage habits of distance education students before the COVID-19 pandemic and during the pandemic period, and to share the acquired results with the researchers over an education term. The study was carried out within the framework of the scanning model, which is one of the scientific research models. In this context, it has been determined as the spring period to include the three-month closure measure applied during the pandemic process as the period to be studied. In order to create the dataset that is the subject of the study, the spring semester weblogs of the 2018-2019 and 2019-2020 academic years on the ÖYS web server were used. In the study, in which the Web usage mining process was followed for analysis processes, a software was developed for data and data analysis processes. With the developed software, comparative analyzes were made on the data obtained about the mobility of the students of the two periods on the system, and the results obtained were shared with the researchers. [ABSTRACT FROM AUTHOR]

In this paper, we review different approaches of providing personalization for cultural heritage content – several static and adaptive approaches, and recommendation systems. The purpose of our research is to compare these methods, evaluate their feasibility for certain types of applications in the field. We outline the needs for personalized experience involving cultural heritage content and describe how those needs are addressed by the different approaches, and point the challenges, benefits, and disadvantages of each of these methods.

Data preprocessing is an essential task to prepare suitable target datasets to apply statistical and data mining algorithms. It has become one of the complex segments in Web usage mining due to the massive and unstructured nature of Web server log. The data preprocessing segment in Web usage mining is divided into several phases such as data fusion, data cleaning, user identification, session identification, path completion, and data formatting. This paper focuses on the initial phases of the Web usage mining process, such as data cleaning, user identification, and session identification. Due to the growing size of log data at terabyte and petabyte scale, traditional data preprocessing algorithms fail at scalability points and possess Big Data issues. During the previous few years, the MapReduce framework has evolved as one of the most used parallel programming frameworks for processing Big Data on a cluster of nodes. In this paper, a MapReduce-based data preprocessing algorithm is developed. This algorithm comprises data preprocessing subphases such as data cleaning, user identification, and session identification. Various efficient heuristics are incorporated into existing MapReduce-based data preprocessing algorithm to detect ethical and unethical robots. Further several experiments are performed on a cluster of nodes and found that the proposed MapReduce-based data preprocessing algorithm is efficient and scalable for larger datasets. Moreover, we have also analyzed the impact of robots' requests on sessions generated in the session identification phase to measure the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Today, e-commerce has become a competitive space for online retailers. Therefore, personalization has become a vital part of e-commerce websites' success, challenging marketers and researchers. This study aims to provide a model for web personalization and mining user interests using a hybrid web usage and web content mining approach. The navigational patterns of web users and the interests of each user on web pages of a Persian website were extracted through web usage mining and topic modeling. Users were then clustered using the dependency distribution algorithm, and 25 categories were extracted. To better understand the behavioral patterns of web users they were categorized using the Support Vector Machine algorithm based on the users' interests and navigational behaviors. The most important result of the proposed system is that the patterns of users' navigation are understandable, and the subsequent analyses will be much simpler. [ABSTRACT FROM AUTHOR]

• Complexity of Pillar 3 disclosures impacts stakeholder interest and preferences. • Readability metrics reveal user preference for less complex documents. • Regulators should prioritize making Pillar 3 disclosures more accessible. This research explores the impact of document complexity and readability on user preferences for disclosure information on commercial bank web portals, with a focus on Pillar 3 disclosures. The study investigates the usage patterns of disclosed information by stakeholders. Through an analysis of web portal access variables and document complexity and readability measures, the study identifies correlations between text complexity/readability and user preferences. The experiment used traditional readability measures like the Gunning Fog Index and Flesch Reading Ease but also focused on less-used metrics such as LIX, various text characteristics, and part-of-speech metrics. The findings reveal that while stakeholders show interest in financial documents despite their complexity, preferences in using these documents vary based on document type and readability. Notably, documents with higher complexity tend to attract more attention, suggesting potential challenges in accessibility and comprehension. The paper highlights the importance of presenting disclosure information in a manner that enhances readability and accessibility to better engage stakeholders. Additionally, the research introduces user preference indicators aligned with web portal access metrics, providing insights into stakeholders' behavior. The research followed up on previous findings of extremely low interest in Pillar 3 information from commercial bank stakeholders. The results showed that stakeholders are more interested in less demanding and more readable texts such as annual reports, and as Pillar 3 documents are more complex and harder to read, they are less interested in them. The research concludes that stakeholders' increasing interest in this information requires finding approaches to presenting it in less complicated and more readable ways. [ABSTRACT FROM AUTHOR]

Users prefer to use various websites like Facebook, Gmail, and YouTube. We can make the system predict what pages we expect in the future and give the users what they have requested. Based on the data gathered and analyzed, we can predict the user's future navigation patterns in response to the user's requests. In order to track down users' navigational sessions, the web access logs created at a specific website are processed. Grouping the user session data is then done into clusters, where inter-cluster similarities are minimized, although the intra-cluster similarities are maximised. Recent clustering and fairness analysis research has focused on centric-based methods such as k-median and kmeans clustering. We propose improved constrained based clustering (ICBC) based on fair algorithms for managing Hierarchical Agglomerative Clustering (HAC) that apply fairness constraints regardless of distance linking parameters, simplifying clustering fairness trials for HAC and intended for various protected groups compared to vanilla HAC techniques. Also, this ICBC is used to select an algorithm whose inherent bias matches a specific problem, and then to adjust the optimization criterion of any distinct algorithm to take the constraints on interpretation to improve the efficiency of clustering. We show that our proposed algorithm finds fairer clustering by evaluation on the NASA dataset by balancing the constraints of the problem. [ABSTRACT FROM AUTHOR]