the future

this year has opened my eyes and widened my horizon, i have always wanted to complete my degree and move into sports marketing, and using the skills and experience gained through this year on digital marketing.

the end of the year

this year has been a very informative and productive year in which i have grown as a digital student to expand my horizon on the internet and in the website design field. Through my participation within Jack downloads i have become more astute about web traffic and web site design as well as understand the way digital marketing is fast becoming the leading tool in marketing taking over from television and print marketing.

Working as a team in jack downloads i learnt the importance of communication within the team and the pitfalls of the lack of communication as evident in the marks for our second publication. However through this adversity and through perseverance and a good team ethic we picked ourselves up and worked together to complete what we had started back in october.

Content based filtering

•Content based filtering recommends items based on a comparison between the content of the two items and a user profile. Each item is represented as a set of descriptors or terms which is usually the words or keywords.

•The user profile is represented with the same terms and built up by analysing the content of items which have been seen by the user.

•Several issues have to be considered when implementing this system. First, terms can either be assigned automatically or manually. When terms are assigned automatically a method has to be chosen that can extract these terms from items.

•Second, the terms have to be represented such that both the user profile and the items can be compared in a meaningful way.

•Third, a learning algorithm has to be chosen that is able to learn the user profile based on seen items and can make recommendations based on this user profile.

Collaborative filtering

•A suitable technology option would be Collaborative filtering which is a common web technique used in generating personalized recommendations.

•It is used by Amazon, Netflix, iTunes and last FM. With iTunes CF works in that all in needs is a database of who has bought what so that it can calculate “people who are like you” so that anytime anyone “who is like you” buys something you have not bought you get a personal recommendation of the song or album.

• CF does not need built in subject knowledge and this makes it easier and less complicated such as Pandora which needs music experts to individualise each song and code a genome to each song.

TRACKR..."we find what you love"

TRACKr is a registered trademark

this is the year long culmination of JACK Downloads, after two successful magazine publications we have made plans to design a mobile app to recommend and find music, movies, tv shows of your choice

The Future of Online Digital Marketing 2012 -2015

the future of online digital marketing 2012 and onwards ....

Evaluating Mr Samasuwo..so far

This course has helped my thinking and the way i look at digital marketing not only as banner ads and viral videos but as SEO, Social Media and many more channels all coming into our homes and lives through television, mobile phones and tablets and computers and laptops. This shows the strength and influence of digital marketing and how as the world becomes more connected how it will become even more powerful within marketing

Personally my researching was my weakest attribute however being part of jack downloads and with the help of Mike Watson i have grown and developed and with it my primary and secondary research has caught up with my silver tongue. Through digital marketing i have published 2 online magazines reviewing the music industry and attended 2 events which have helped me liase with proffessionals and masterclasses with Dave Chaffey.