History and Personalisation

This functionality provides users with an updated view of the system and communicates any recent updates that have occurred since the user’s last interaction with the system. Together with this, a view indicating the trending searches and browses was implemented.


The history was implemented by making use of the Cookies functionality provided by browsers. The implementation involved using cookies to obtain information as to when the user had last visited the archive. This information is then used to compare with the digital objects' properties in order to retrieve items that have been modified since the user’s last interaction. These updates are categorised as per the browsing categories and the interface allows the user to filter out the updates dependent on the category selected.

Functionality to indicate trending searches was implemented with the use of a tag cloud. The system would keep record of the searches and browsing conducted throughout the archive by all the users of the archive. This information was recorded in a text file stored on the server and the content of the text file was used to construct the tag cloud.

Figure 1: History and Personalisation interface

Overall Browse

This Figure illustrates the History and Personalisation interface where the left hand side depicts the categories that the updated items fall into; the middle column is a grid view of the items that have been updated; and the right hand side is a tagcloud indicating the trending searches and browses.

Evaluation and Results

This was evaluated using User Acceptance Testing as well as receiving qualitative feedback and comments from the users. The client's accepted the product and indicated that they understood the functionality provided and found it useful. The functional requirements assessed and accepted are as below.
History Functional Requirements
Functionality Pass or Fail
Tagcloud Pass
Filtering recent updates Pass

Future Work

Future work pertaining to the history and personalisation services could involve actual recommendation algorithms used to provide users with a personalised experience by observing the crowd and similar user profiles.

Copyright © 2015 Nicole Petersen, Noosrat Hossain and Noxolo Mthimulu