9,366 research outputs found
The Metabolism and Growth of Web Forums
We view web forums as virtual living organisms feeding on user's attention
and investigate how these organisms grow at the expense of collective
attention. We find that the "body mass" () and "energy consumption" ()
of the studied forums exhibits the allometric growth property, i.e., . This implies that within a forum, the network transporting
attention flow between threads has a structure invariant of time, despite of
the continuously changing of the nodes (threads) and edges (clickstreams). The
observed time-invariant topology allows us to explain the dynamics of networks
by the behavior of threads. In particular, we describe the clickstream
dissipation on threads using the function , in which
is the clickstreams to node and is the clickstream dissipated
from . It turns out that , an indicator for dissipation efficiency,
is negatively correlated with and sets the lower boundary
for . Our findings have practical consequences. For example,
can be used as a measure of the "stickiness" of forums, because it quantifies
the stable ability of forums to convert into , i.e., to remain users
"lock-in" the forum. Meanwhile, the correlation between and
provides a convenient method to evaluate the `stickiness" of forums. Finally,
we discuss an optimized "body mass" of forums at around that minimizes
and maximizes .Comment: 6 figure
Leverage web analytics for real time website browsing recommendations
Trabalho apresentado no 5th World Conference on Information Systems and Technologies (WorldCISTâ17), 11-13 de abril 2017, Porto Santo, Madeira PortugalAs a websitesâ structure grow it is paramount to accommodate the
alignment of user needs and experience with the overall websitesâ purposes.
Toward this requirement, the proposed website navigation recommendation
system suggests to users, pages that might be of her interest based on past
successful navigation patterns of overall siteâs usage. Most of existing
recommendation systems adopts traditionally one of the web mining branches.
We take a different stance, on web mining usage, and alternatively considered
the real time enactment of web analytic tools supported analysis given their
current maturity and affordances. On this basis we provide a model, its
implementation and evaluation for navigation based recommendations
generation and delivery. The developed prototype adopted a SaaS orientation
to promote the underlying functionalities integration within any website.
Preliminary evaluationâs results seem to favor the validation of the present
contribution rational.N/
Web Site Personalization based on Link Analysis and Navigational Patterns
The continuous growth in the size and use of the World Wide Web imposes new methods of design and development of on-line information services. The need for predicting the usersâ needs in order to improve the usability and user retention of a web site is more than evident and can be addressed by personalizing it. Recommendation algorithms aim at proposing ânextâ pages to users based on their current visit and the past usersâ navigational patterns. In the vast majority of related algorithms, however, only the usage data are used to produce recommendations, disregarding the structural properties of the web graph. Thus important â in terms of PageRank authority score â pages may be underrated. In this work we present UPR, a PageRank-style algorithm which combines usage data and link analysis techniques for assigning probabilities to the web pages based on their importance in the web siteâs navigational graph. We propose the application of a localized version of UPR (l-UPR) to personalized navigational sub-graphs for online web page ranking and recommendation. Moreover, we propose a hybrid probabilistic predictive model based on Markov models and link analysis for assigning prior probabilities in a hybrid probabilistic model. We prove, through experimentation, that this approach results in more objective and representative predictions than the ones produced from the pure usage-based approaches
Soft behaviour modelling of user communities
A soft modelling approach for describing behaviour in on-line user communities is introduced in this work. Behaviour models of individual users in dynamic virtual environments have been described in the literature in terms of timed transition automata; they have various drawbacks. Soft multi/agent behaviour automata are defined and proposed to describe multiple user behaviours and to recognise larger classes of user group histories, such as group histories which contain unexpected behaviours. The notion of deviation from the user community model allows defining a soft parsing process which assesses and evaluates the dynamic behaviour of a group of users interacting in virtual environments, such as e-learning and e-business platforms. The soft automaton model can describe virtually infinite sequences of actions due to multiple users and subject to temporal constraints. Soft measures assess a form of distance of observed behaviours by evaluating the amount of temporal deviation, additional or omitted actions contained in an observed history as well as actions performed by unexpected users. The proposed model allows the soft recognition of user group histories also when the observed actions only partially meet the given behaviour model constraints. This approach is more realistic for real-time user community support systems, concerning standard boolean model recognition, when more than one user model is potentially available, and the extent of deviation from community behaviour models can be used as a guide to generate the system support by anticipation, projection and other known techniques. Experiments based on logs from an e-learning platform and plan compilation of the soft multi-agent behaviour automaton show the expressiveness of the proposed model
A Review Paper on Web Usage Mining and future request prediction
Abstract:-Web usage mining is the application of data mining techniques to web log files in order to extract the useful patterns. The Web usage mining includes the data from the web server logs, poxy server logs, browser logs, user profiles, registration data, user sessions or transactions, cookies, user profiles, registration data and any other data as the results of interactions.With the continued growth and proliferation of Web services and Web based information systems, the volumes of user data have reached astronomical proportions. Analyzing such data using Web Usage Mining can help to determine the visiting interests or needs of the web user. Lots of research has been done in this field but this paper deals with user future request prediction using web log record or user information. This paper gives the overview of various methods of future request prediction
A survey of temporal knowledge discovery paradigms and methods
With the increase in the size of data sets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, interest in temporal databases has been increasing and a growing number of both prototype and implemented systems are using an enhanced temporal understanding to explain aspects of behavior associated with the implicit time-varying nature of the universe. This paper investigates the confluence of these two areas, surveys the work to date, and explores the issues involved and the outstanding problems in temporal data mining
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