| الملخص: |
Abstract: Today’s users are spending more time on web applications. Many users browse web applications and navigate through different web pages. They may have different interests, especially when it comes to large-scale applications. The more the developers of the applications know about their users’ needs and interests, the smarter choices they will make for their application’s development. Inferring a behavioral model from users’ navigation patterns in a web application helps application providers to understand their users’ interests. A navigational pattern is a record of where a user visits; the pattern is extracted from the start to the end of a user session. User navigation information is obtained by collecting the data in a log file. Some studies instrumented the application’s web pages to collect data and then model user navigational behaviors. To instrument a web page, the source code of the program is modified with additional commands. However, this can be difficult when the source code is inaccessible. Ideally, a user behavioral inference process should not be required to instrument the application’s web pages to generate a user behavioral model. Also, a model generation approach needs to support the evolution of web applications. A behavioral model should be generated incrementally during its evolution and should play a role in the application’s evolution (upgrading) procedure. This can help sustain web applications. Inferring a model by predicting and analyzing users’ navigational behaviors isiii necessary to understand users’ interests. Developers can identify interesting (from users’ perspective) or problematic pages of applications and therefore improve the application design. Analyzing the behavioral model helps to detect design anomalies such as dead-ends; pages in which users are being prevented from leaving the page without closing it. Detecting dead-ends can significantly help in addressing design anomalies and providing solutions to retain users. Satisfied customers are more likely to stay with the company and contribute to its success. It is ideal to analyze web pages to model user behaviors. Web page analysis methods utilize web page segmentation which is the process of segmenting a web page into different blocks, where each block contains similar components in terms of structural, visual, or contextual similarity. Current segmentation methods use the Document Object Model (DOM) structure of a web page and vision-based techniques to segment a web page. However, current methods do not consider semantic analysis to categorize pages. Semantic analysis includes extracting text from segmented blocks, computing textual similarity, and regrouping blocks. In this research, we attempt to bridge several gaps in all the above-mentioned areas. Firstly, we provide an automated approach, with no instrumentation, to generate user behavioral models. We evaluate the utility of our approach by using it on a large-scale mobile and desktop application. Also, we evaluate the evolving properties of interaction patterns against the inferred behavioral models using an analysis engine. Next, we present a new combination model of web page segmentation, namelyiv Fusion-Block, by dividing the content of a web page into blocks by initially considering human perception (inspired by Gestalt laws of grouping) and subsequentially re-segmenting initial similar blocks using semantic text similarity. Hence, in the next part of our research, we improve the segmentation model, namely Integrated-Block, by merging the DOM structure, vision-based, and textbased similarity metrics of web pages. Finally, to verify the effectiveness of our approach, we applied it to the public datasets and compared it with the five existing state-of-the-art algorithms. We demonstrate the value and novelty of the presented solutions using extensive evaluations throughout the thesis. |
