Abstract:
The population of users on the internet has increased steadily in the past years, and online
content delivery platforms continuously generate ways in which content can be delivered to
their users in a better and more efficient manner. Part of the necessity for this has to with the
fact that the rate at which information is uploaded to the internet is overwhelmingly high,
making it difficult in sorting through the numerous amounts of data available. For this reason,
the rise of Recommendation Systems has been noticed. Much of the content that users decide
to view is often delivered to them by recommender systems. These systems not only aid in
helping users sort through data easily, but they also provide a great deal of revenue to most
content providers. This research aims to identify a way in which current recommender systems
can be lacking in certain functionality, and propose and implement a technology that can rectify
this gap and be used for recommendations in ways which will be more beneficial to its users.
The system developed through the research allows its users to have more direct control over
their recommendations by allowing them to pick the data they want recommendations on, as
well as allow files and data on their local system storage to be used to gather recommended
content, something which cannot be done with commercial current systems. Testing performed
on the resulting system has shown how this system can be beneficial to users.
