  Given the effectiveness and ease of use, Item-based Collaborative Filtering
(ICF) methods have been broadly used in industry in recent years. The key of
ICF lies in the similarity measurement between items, which however is a
coarse-grained numerical value that can hardly capture users' fine-grained
preferences toward different latent aspects of items from a representation
learning perspective. In this paper, we propose a model called REDA (latent
Relation Embedding with Dual Attentions) to address this challenge. REDA is
essentially a deep learning based recommendation method that employs an item
relation embedding scheme through a neural network structure for inter-item
relations representation. A relational user embedding is then proposed by
aggregating the relation embeddings between all purchased items of a user,
which not only better characterizes user preferences but also alleviates the
data sparsity problem. Moreover, to capture valid meta-knowledge that reflects
users' desired latent aspects and meanwhile suppress their explosive growth
towards overfitting, we further propose a dual attentions mechanism, including
a memory attention and a weight attention. A relation-wise optimization method
is finally developed for model inference by constructing a personalized ranking
loss for item relations. Extensive experiments are implemented on real-world
datasets and the proposed model is shown to greatly outperform state-of-the-art
methods, especially when the data is sparse.
