Serendipitous recommendations via innovators

Abstract

To realize services that provide serendipity, this paper assesses the surprise of each user when presented recommendations. We propose a recommendation algorithm that focuses on the search time that, in the absence of any recommendation, each user would need to find a desirable and novel item by himself. Following the hypothesis that the degree of user's surprise is proportional to the estimated search time, we consider both innovators' preferences and trends for identifying items with long estimated search times. To predict which items the target user is likely to purchase in the near future, the candidate items, this algorithm weights each item that innovators have purchased and that reflect one or more current trends; it then lists them in order of decreasing weight. Experiments demonstrate that this algorithm outputs recommendations that offer high user/item coverage, a low Gini coefficient, and long estimated search times, and so offers a high degree of recommendation serendipitousness.