Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation

by   Chen Ma, et al.
HUAWEI Technologies Co., Ltd.
McGill University

Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them. Although matrix factorization and deep learning based methods have proved effective in user preference modeling, they violate the triangle inequality and fail to capture fine-grained preference information. To tackle this, we develop a distance-based recommendation model with several novel aspects: (i) each user and item are parameterized by Gaussian distributions to capture the learning uncertainties; (ii) an adaptive margin generation scheme is proposed to generate the margins regarding different training triplets; (iii) explicit user-user/item-item similarity modeling is incorporated in the objective function. The Wasserstein distance is employed to determine preferences because it obeys the triangle inequality and can measure the distance between probabilistic distributions. Via a comparison using five real-world datasets with state-of-the-art methods, the proposed model outperforms the best existing models by 4-22 recommendation.


User Diverse Preference Modeling by Multimodal Attentive Metric Learning

Most existing recommender systems represent a user's preference with a f...

Knowledge-Enhanced Top-K Recommendation in Poincaré Ball

Personalized recommender systems are increasingly important as more cont...

Collaborative Translational Metric Learning

Recently, matrix factorization-based recommendation methods have been cr...

Enhancing Factorization Machines with Generalized Metric Learning

Factorization Machines (FMs) are effective in incorporating side informa...

Adversarial Mahalanobis Distance-based Attentive Song Recommender for Automatic Playlist Continuation

In this paper, we aim to solve the automatic playlist continuation (APC)...

Modeling Personalized Item Frequency Information for Next-basket Recommendation

Next-basket recommendation (NBR) is prevalent in e-commerce and retail i...

A Novel User Representation Paradigm for Making Personalized Candidate Retrieval

Candidate retrieval is a crucial part in recommendation system, where qu...

Please sign up or login with your details

Forgot password? Click here to reset