Latent Unexpected and Useful Recommendation

TL;DR

This paper introduces a novel latent space approach for unexpected recommendations in recommender systems, leveraging a Latent Convex Hull method to better capture complex user-item interactions and improve the novelty of suggestions.

Contribution

The paper proposes a new latent space modeling technique, Latent Convex Hull, to enhance unexpected recommendation quality beyond existing feature space methods.

Findings

Outperforms state-of-the-art unexpected recommendation methods

Achieves higher unexpectedness measures while maintaining accuracy

Demonstrates effectiveness on real-world datasets

Abstract

Providing unexpected recommendations is an important task for recommender systems. To do this, we need to start from the expectations of users and deviate from these expectations when recommending items. Previously proposed approaches model user expectations in the feature space, making them limited to the items that the user has visited or expected by the deduction of associated rules, without including the items that the user could also expect from the latent, complex and heterogeneous interactions between users, items and entities. In this paper, we define unexpectedness in the latent space rather than in the feature space and develop a novel Latent Convex Hull (LCH) method to provide unexpected recommendations. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed model that significantly outperforms alternative state-of-the-art unexpected recommendation methods in terms of unexpectedness measures while achieving the same level of accuracy.