DeepAI AI Chat
Log In Sign Up

Latent Unexpected and Useful Recommendation

by   Pan Li, et al.
NYU college

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.


page 1

page 2

page 3

page 4


Latent Unexpected Recommendations

Unexpected recommender system constitutes an important tool to tackle th...

PURS: Personalized Unexpected Recommender System for Improving User Satisfaction

Classical recommender system methods typically face the filter bubble pr...

Deep Unified Representation for Heterogeneous Recommendation

Recommendation system has been a widely studied task both in academia an...

Recommender Systems with Heterogeneous Side Information

In modern recommender systems, both users and items are associated with ...

Episodes Discovery Recommendation with Multi-Source Augmentations

Recommender systems (RS) commonly retrieve potential candidate items for...

A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations

We present a novel dynamic recommendation model that focuses on users wh...

Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation

Point-of-Interest (POI) recommender systems play a vital role in people'...