Preference or Intent? Double Disentangled Collaborative Filtering
Chao Wang Hengshu Zhu Dazhong Shen Wei wu Hui Xiong
People usually have different intents for choosing items, while their
preferences under the same intent may also different. In traditional
collaborative filtering approaches, both intent and preference factors are
usually entangled in the modeling process, which significantly limits the
robustness and interpretability of recommendation performances. For example,
the low-rating items are always treated as negative feedback while they
actually could provide positive information about user intent. To this end, in
this paper, we propose a two-fold representation learning approach, namely
Double Disentangled Collaborative Filtering (DDCF), for personalized
recommendations. The first-level disentanglement is for separating the
influence factors of intent and preference, while the second-level
disentanglement is performed to build independent sparse preference
representations under individual intent with limited computational complexity.
Specifically, we employ two variational autoencoder networks, intent
recognition network and preference decomposition network, to learn the intent
and preference factors, respectively. In this way, the low-rating items will be
treated as positive samples for modeling intents while the negative samples for
modeling preferences. Finally, extensive experiments on three real-world
datasets and four evaluation metrics clearly validate the effectiveness and the
interpretability of DDCF.