Design of a Serendipity-Incorporated Recommender System

Unexpected yet advantageous findings, often referred to as serendipitous discoveries, are becoming increasingly significant in the field of computer science. This research aims to examine the impact of factors that could potentially trigger such serendipity within a recommender system (RS) and consequently proposes a novel, serendipity-incorporated recommender system (SRS). The SRS is developed by integrating elements that could stimulate the occurrence of serendipity into an RS algorithm. These elements include interestingness, diversity, and unexpectedness. As a result, the SRS is equipped to provide users with recommendations that are surprising, intriguing, and atypical. The algorithm within the SRS recommends three items predicated on a user's preferred item. To facilitate the selection of items to be recommended, we have designed a computation method called the 'serendipity measure', which is tasked with calculating the weights of all items. Our innovative algorithm and its efficient execution are expounded upon extensively in this study. The performance of the SRS was assessed using a quantitative serendipity evaluation model (QSEM). This model is a quantitative tool designed to measure the probability of users encountering serendipitous events within a specific information space. We conducted a user study to compare the SRS with the traditional cold-start recommender system (CRS), and the feedback for the SRS was positively received. The experiments confirm the viability of cultivating a serendipitous environment from a system's perspective. The test results also underline the exciting potential that serendipity brings to recommender systems.