3 research outputs found
Joint Training Capsule Network for Cold Start Recommendation
This paper proposes a novel neural network, joint training capsule network
(JTCN), for the cold start recommendation task. We propose to mimic the
high-level user preference other than the raw interaction history based on the
side information for the fresh users. Specifically, an attentive capsule layer
is proposed to aggregate high-level user preference from the low-level
interaction history via a dynamic routing-by-agreement mechanism. Moreover,
JTCN jointly trains the loss for mimicking the user preference and the softmax
loss for the recommendation together in an end-to-end manner. Experiments on
two publicly available datasets demonstrate the effectiveness of the proposed
model. JTCN improves other state-of-the-art methods at least 7.07% for
CiteULike and 16.85% for Amazon in terms of Recall@100 in cold start
recommendation.Comment: Accepted by SIGIR'2
ColdGAN: Resolving Cold Start User Recommendation by using Generative Adversarial Networks
Mitigating the new user cold-start problem has been critical in the
recommendation system for online service providers to influence user experience
in decision making which can ultimately affect the intention of users to use a
particular service. Previous studies leveraged various side information from
users and items; however, it may be impractical due to privacy concerns. In
this paper, we present ColdGAN, an end-to-end GAN based model with no use of
side information to resolve this problem. The main idea of the proposed model
is to train a network that learns the rating distributions of experienced users
given their cold-start distributions. We further design a time-based function
to restore the preferences of users to cold-start states. With extensive
experiments on two real-world datasets, the results show that our proposed
method achieves significantly improved performance compared with the
state-of-the-art recommenders
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation
Highly skewed long-tail item distribution is very common in recommendation
systems. It significantly hurts model performance on tail items. To improve
tail-item recommendation, we conduct research to transfer knowledge from head
items to tail items, leveraging the rich user feedback in head items and the
semantic connections between head and tail items. Specifically, we propose a
novel dual transfer learning framework that jointly learns the knowledge
transfer from both model-level and item-level: 1. The model-level knowledge
transfer builds a generic meta-mapping of model parameters from few-shot to
many-shot model. It captures the implicit data augmentation on the model-level
to improve the representation learning of tail items. 2. The item-level
transfer connects head and tail items through item-level features, to ensure a
smooth transfer of meta-mapping from head items to tail items. The two types of
transfers are incorporated to ensure the learned knowledge from head items can
be well applied for tail item representation learning in the long-tail
distribution settings. Through extensive experiments on two benchmark datasets,
results show that our proposed dual transfer learning framework significantly
outperforms other state-of-the-art methods for tail item recommendation in hit
ratio and NDCG. It is also very encouraging that our framework further improves
head items and overall performance on top of the gains on tail items.Comment: Accepted by WWW 2021 as a long pape