Uncovering Recommendation Serendipity with Objective Data-driven Factor Investigation

The serendipity recommendation tries to burst the filter bubble while still meeting user interests. However, serendipity itself has not been well understood in the recommendation system. Thus, factor investigation in recommendation serendipity has attracted much attention, for which two challenges hinder follow-up research: (1) Ambiguity of factors . Different works exploit different factors, and the meanings of factors are inconsistent in various works. (2) Lack of complete impact validation . The importance of these factors in different domains is not yet fully understood. The common approach of user surveys costs much, but the results are usually less objective and limited in quantity. To this end, we strive to comprehensively identify and clarify serendipity factors and explore objective data-driven approaches to validate factor impacts in large-scale cross-domain scenarios. We first conduct a comprehensive literature review to identify all possible factors, from which we find that some factors are being used indistinguishably. To address this issue, we propose two principles of meaning coverage and factor independence to clarify and disentangle serendipity factors. Next, we propose a general experimental framework to explore the impacts of factors. Then, we implement one such framework and run experiments on nine representative datasets to study factor importance on serendipity. We also propose a quantitative method to measure the degree of disentanglement of factors and to test the effects of factor combinations. We gain several useful findings: (1) relevance , diversity , and random are critical factors affecting serendipity; (2) domain features affect factor importance and can guide serendipity recommendation; (3) the disentanglement quantification method benefits the understanding of serendipity and the combination of factors. To our knowledge, this is the first work to comprehensively investigate serendipity factors and experimentally compare their impacts in an objective data-driven approach.