Enhanced content-based filtering algorithm using Artificial Bee Colony optimisation

Recommender systems can guide the users in a tailored way to interesting objects in a large space of possible options. The Content-based Filtering (CBF) approach is one of the most widely adapted to date. It analyses a set of textual descriptions of items. These items are already evaluated by an interactive user in prior steps. It then builds a model or profile of this user. The profile is then exploited to suggest a new item. Unfortunately, filtering in this method is mainly used for recommending only one item at a time. The research here considers how this component can propose a list of items to a user from large amounts of data. We enhance the Content-based Filtering algorithm in order to explore a huge data set and return a list of recommendations rather than just rating an item. For this, an Artificial Bee Colony (ABC) technique has been adapted and applied to the CBF method. ABC is one of the efficient Evolutionary Computing techniques that are used in solving optimization problems.