Reinforcement learning over sentiment-augmented knowledge graphs towards accurate and explainable recommendation
Explainable recommendation has gained great attention in recent years. A lot of work in this
research line has chosen to use the knowledge graphs (KG) where relations between …
research line has chosen to use the knowledge graphs (KG) where relations between …
AR-CF: Augmenting virtual users and items in collaborative filtering for addressing cold-start problems
Cold-start problems are arguably the biggest challenges faced by collaborative filtering (CF)
used in recommender systems. When few ratings are available, CF models typically fail to …
used in recommender systems. When few ratings are available, CF models typically fail to …
Asine: Adversarial signed network embedding
Motivated by a success of generative adversarial networks (GAN) in various domains
including information retrieval, we propose a novel signed network embedding framework …
including information retrieval, we propose a novel signed network embedding framework …
Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time
In video recommendation, an ongoing effort is to satisfy users' personalized information
needs by leveraging their logged watch time. However, watch time prediction suffers from …
needs by leveraging their logged watch time. However, watch time prediction suffers from …
Effective and efficient negative sampling in metric learning based recommendation
J Park, YC Lee, SW Kim - Information Sciences, 2022 - Elsevier
In this paper, we start by pointing out the problem of a negative sampling (NS) strategy,
denoted as nearest-NS (NNS), used in metric learning (ML)-based recommendation …
denoted as nearest-NS (NNS), used in metric learning (ML)-based recommendation …
Serenade-low-latency session-based recommendation in e-commerce at scale
Session-based recommendation predicts the next item with which a user will interact, given
a sequence of her past interactions with other items. This machine learning problem targets …
a sequence of her past interactions with other items. This machine learning problem targets …
Learning to distinguish multi-user coupling behaviors for tv recommendation
This paper is concerned with TV recommendation, where one major challenge is the
coupling behavior issue that the behaviors of multiple users are coupled together and not …
coupling behavior issue that the behaviors of multiple users are coupled together and not …
M-BPR: A novel approach to improving BPR for recommendation with multi-type pair-wise preferences
In this paper, we examine the two assumptions of the Bayesian personalized ranking (BPR),
a well-known pair-wise method for one-class collaborative filtering (OCCF):(1) a user with …
a well-known pair-wise method for one-class collaborative filtering (OCCF):(1) a user with …
AiRS: a large-scale recommender system at naver news
Online news providers such as Google News, Bing News, and NAVER News collect a large
number of news articles from a variety of presses and distribute these articles to users via …
number of news articles from a variety of presses and distribute these articles to users via …
Mascot: A quantization framework for efficient matrix factorization in recommender systems
In recent years, quantization methods have successfully accelerated the training of large
deep neural network (DNN) models by reducing the level of precision in computing …
deep neural network (DNN) models by reducing the level of precision in computing …