Acquisition and Modelling of Short-TermUser Behaviour on theWeb: A Survey

Abstract

User behaviour in data intensive applications such as the Web-based applications represents a complex set of actions influenced by plenty of factors. Thanks to this complexity, it is extremely hard for human to be able to understand all its aspects. Despite of this, by observing user actions from multiple views, we are able to extract and to model typical behaviour and its deviations on the Web. The website itself, together with transaction server logs, includes information about the site structure, content and about the actual user actions (clicks) within the site. User actions logically reflect the behaviour, while other sources indicate his/her context. Combination of these data sources allows to model the typical user behaviour and his/her preferences. The longterm behaviour describes relatively stable user preferences based on extensive user history. As the Web has become more and more dynamic, modelling user behaviour from the long-term perspective does not satisfy requirements of current Web based applications. On the other side, the short-term behaviour describes current user activity and his/her actual intent. However, this source of information is often noisy. To address these shortcomings the state-ofthe-art combines both perspectives, which allows to meaningful and timely modelling of user behaviour. In this paper, we provide a comprehensive survey

A Study on Visual Expression Elements and User Satisfaction in Video Streaming Services on the Web: Focusing on Video Thumbnails

“…The content is the main reason why a user visits the website and its pages. Its quality affects the visit duration and future user return [5]. YouTube has a rapidly increasing variety of contents which means the users do not know which video to choose.…”

Long-Tail Distribution Financial Recommender Systems Embedded into Social Networks

“…Recently, the analysis of the behavior of network users is carried out using a graph that allows you to identify the structure of the site and find hidden connections that can affect the behavior of network users. In early studies, the structure of a website was usually represented as a multidigraph containing directed edges, which can have multiple edges between the same pair of vertices (Kassak et al, 2018). The analysis allows you to identify unique addresses that provide new content to include recommendations in the system.…”