Introducing Serendipity in Recommender Systems through Collaborative Methods

Widely used recommendation systems are mainly accuracy-oriented since they are based on item-based ratings and useror item-based similarity measures. Such accuracy-based engines do not consider factors such as proliferation of varied user interests and the desire for changes. This results in a muted user experience that is generated from a constrained and narrow feature set. Recommender systems should therefore consider other important metrics outside of accuracy such as coverage, novelty, serendipity, unexpectedness and usefulness. The main focus of this thesis is to both incorporate serendipity into a recommendation engine and improve its quality using the widely used collaborative filtering method. Serendipity is defined as finding something good or useful while not specifically searching for it. The design of recommendation engines that considers serendipity is a relatively new and an open research problem. This is largely due to a certain degree of ambiguity in balancing the level of unexpectedness and usefulness of items. In this thesis, a new hybrid algorithm that combines a standard user-based collaborative filtering method, and item attributes has been proposed to improve the quality of serendipity over those that use item ratings alone. The algorithm was implemented using Python in conjunction with the scientific computing package NumPy. Furthermore, the code has been validated using a well-accepted and widely used open source software namely, Apache Mahout, that provides support for recommender system application development. The new method has been tested on the 100K MovieLens dataset from the GroupLens Research Center that consists of 100,000 preferences for 1,682 movies rated by 943 customers. The new algorithm is shown to be capable of identifying a significant fraction of movies that are less serendipitous but which might not have been identified otherwise, thereby improving the quality of predictions.