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.

Serendipitous Personalized Ranking for Top-N Recommendation

“…[17] introduced TANGENT, which is based on a graph mining technique to provide "Surprise-me" recommendations. In [18], the author proposed an algorithm which focused on the search time each user would need to find a desirable and novel item by him/herself. Murakami et al [19] and Ge et al [20] captured the two essential aspects of serendipity: unexpectedness and usefulness.…”

Improved Neighborhood Search for Collaborative Filtering

“…A study conducted in the field of retail and marketing shows that consumers regard expert opinions as more reliable [8]. In agreement with this observation, several recent studies have exploited the knowledge of experts [9][10][11][12][13][14][15][16][17][18]. Those approaches are based on the assumption that users with more expertise may give more useful information, which will lead to more accurate recommendations.…”

An investigation on the serendipity problem in recommender systems

“…Some collaborative approaches have been developed as well. In (Kawamae, 2010;Kawamae, Sakano, & Yamada, 2009), the authors propose a strategy for suggesting surprisingly interesting items to a user by identifying purchase history logs of users who have similar preferences and a high degree of purchase precedence (i.e., purchasing the same items earlier) relative to that user. These users are called ''innovators'' since they become aware of items well before their release, and purchase them soon after their release.…”