198 research outputs found
A Survey on Web Usage Mining
Now a day World Wide Web become very popular and interactive for transferring of information. The web is huge, diverse and active and thus increases the scalability, multimedia data and temporal matters. The growth of the web has outcome in a huge amount of information that is now freely offered for user access. The several kinds of data have to be handled and organized in a manner that they can be accessed by several users effectively and efficiently. So the usage of data mining methods and knowledge discovery on the web is now on the spotlight of a boosting number of researchers. Web usage mining is a kind of data mining method that can be useful in recommending the web usage patterns with the help of users2019; session and behavior. Web usage mining includes three process, namely, preprocessing, pattern discovery and pattern analysis. There are different techniques already exists for web usage mining. Those existing techniques have their own advantages and disadvantages. This paper presents a survey on some of the existing web usage mining techniques
Crowdsourced real-world sensing: sentiment analysis and the real-time web
The advent of the real-time web is proving both challeng-
ing and at the same time disruptive for a number of areas of research,
notably information retrieval and web data mining. As an area of research reaching maturity, sentiment analysis oers a promising direction for modelling the text content available in real-time streams. This paper reviews the real-time web as a new area of focus for sentiment analysis
and discusses the motivations and challenges behind such a direction
Domain-specific queries and Web search personalization: some investigations
Major search engines deploy personalized Web results to enhance users'
experience, by showing them data supposed to be relevant to their interests.
Even if this process may bring benefits to users while browsing, it also raises
concerns on the selection of the search results. In particular, users may be
unknowingly trapped by search engines in protective information bubbles, called
"filter bubbles", which can have the undesired effect of separating users from
information that does not fit their preferences. This paper moves from early
results on quantification of personalization over Google search query results.
Inspired by previous works, we have carried out some experiments consisting of
search queries performed by a battery of Google accounts with differently
prepared profiles. Matching query results, we quantify the level of
personalization, according to topics of the queries and the profile of the
accounts. This work reports initial results and it is a first step a for more
extensive investigation to measure Web search personalization.Comment: In Proceedings WWV 2015, arXiv:1508.0338
Revisiting Resolution and Inter-Layer Coupling Factors in Modularity for Multilayer Networks
Modularity for multilayer networks, also called multislice modularity, is
parametric to a resolution factor and an inter-layer coupling factor. The
former is useful to express layer-specific relevance and the latter quantifies
the strength of node linkage across the layers of a network. However, such
parameters can be set arbitrarily, thus discarding any structure information at
graph or community level. Other issues are related to the inability of properly
modeling order relations over the layers, which is required for dynamic
networks.
In this paper we propose a new definition of modularity for multilayer
networks that aims to overcome major issues of existing multislice modularity.
We revise the role and semantics of the layer-specific resolution and
inter-layer coupling terms, and define parameter-free unsupervised approaches
for their computation, by using information from the within-layer and
inter-layer structures of the communities. Moreover, our formulation of
multilayer modularity is general enough to account for an available ordering of
the layers and relating constraints on layer coupling. Experimental evaluation
was conducted using three state-of-the-art methods for multilayer community
detection and nine real-world multilayer networks. Results have shown the
significance of our modularity, disclosing the effects of different
combinations of the resolution and inter-layer coupling functions. This work
can pave the way for the development of new optimization methods for
discovering community structures in multilayer networks.Comment: Accepted at the IEEE/ACM Conf. on Advances in Social Network Analysis
and Mining (ASONAM 2017
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