gRecommendation
engines help users by providing customized write ups suggestions by comparing
stuff with the searched history done by users available on the website. It is a
very unique and ubiquitous feature provided by the section fronts and home page
on websites. Considering from a technical upfront, it is a game of turning and transforming
log data that is consistently cascading in from innumerous public facing web
servers into comprehensible and logical browsing history, building a reading
profile entirely based upon the Metadata associated with each article in that history.
Further, compilation could be done of all the recommended lists used by each
user and make those list available through a service that can manage umpteen requests
per second applicable by millions and zillions of different users. Afterwards,
an automated solution has to be designed and implemented on a huge cluster of
boxes using a particular tool for primary storage and consolidation. The big data recommendation engine system uses Hadoop technology through elastic
MapReduce with tailored Java based logs to reduce and help them to transform
the logs, C procedures to read and write highly optimized information and data
files and a node. The recommendation algorithm takes benefit of the rich
Metadata associated, along with to ascertain that there are many potentially
and useful methodologies to discover and browse more. Recommendations version 2
is already in the staging process featuring dynamic and dramatic improvisations
in both the time a web page hit takes to influence the technology and effectiveness
of the algorithm in terms of computational resources. If resources as well as
time will allow, there would definitely be a revamp in the recommendation
algorithm that is applied to improvise the quality and effectuality of the
recommendations. There is no as such best and ideal way to do the same.
Recently Google has
broadened its reach and horizon of its Google+ marketplace, and the company is introducing
a new mobile content recommendation technology service empowered by Google+. Such
recommendations will not only appear as small widgets at the bottom side of the
screen but also be browsed by users as a news website that has enabled this
service. The launching partner of the company this time is Forbes, and other
people can easily integrate this functionality by simply adding a single line
of code to their mobile websites. Publishers and advertisers can manage and
very well handle recommendation widgets from their respective Google+ accounts.
From that platform only, they can figure out and decide when should the widgets
should appear and handle a list of web pages and where the widgets should not
appear along with the list of pages that should never ever come up in
recommendations.
With time, more
and more firms are making use of recommendation engines. For example, Apple has
its own engine helping users to find out applications to enjoy and discover
more about Apple’s large inventory. Many of the recommendation algorithms used in
engines as well as for machine learning process are not at all new and unique. The
well known and acknowledged constraints along with the appropriate usages include
decision trees, regression, support vector machines, K nearest neighbor, naïve Bayes
and neural networks etc. Many of these methodologies have been applied and used
to affirm data driven business decisive making for a long period of time. So,
if this is the case what is the actual scenario because of which more and more
companies are choosing to implement recommendation engines to affirm effective
and sound decision making. There are key trends drifting the focus from robust
and scalable to enable big data recommendation engines to scale economically, efficiently
and technologically. Basically, they put data on an immediate basis to work for
consumers as well as for business purposes. The opportunities for their
implementation have been improvised and obstacles have reduced down. Adding
more, more and more firms are figuring out ways to cost a small fraction of average
advertising campaigns, bringing in directly attributable revenues as well as
render amazingly short payback results if done in an apt manner.
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