Latent Unexpected Recommendations
This work addresses the challenge of providing unexpected yet satisfying recommendations for users, representing an incremental improvement over prior methods.
The paper tackles the problem of filter bubbles and user boredom in recommender systems by proposing a method to model unexpectedness in the latent space of user and item embeddings, resulting in a significant increase in unexpectedness measure without sacrificing accuracy across three real-world datasets.
Unexpected recommender system constitutes an important tool to tackle the problem of filter bubbles and user boredom, which aims at providing unexpected and satisfying recommendations to target users at the same time. Previous unexpected recommendation methods only focus on the straightforward relations between current recommendations and user expectations by modeling unexpectedness in the feature space, thus resulting in the loss of accuracy measures in order to improve unexpectedness performance. Contrast to these prior models, we propose to model unexpectedness in the latent space of user and item embeddings, which allows to capture hidden and complex relations between new recommendations and historic purchases. In addition, we develop a novel Latent Closure (LC) method to construct hybrid utility function and provide unexpected recommendations based on the proposed model. Extensive experiments on three real-world datasets illustrate superiority of our proposed approach over the state-of-the-art unexpected recommendation models, which leads to significant increase in unexpectedness measure without sacrificing any accuracy metric under all experimental settings in this paper.