PandaCid's Blog

This paper is a direct extension of this paper . It adds an explanation task objective and jointly training both rating prediction and explanation tasks. Please refer my other post for multi-pointer co-attention learning. Figure 1: Co-Attentive Multi-Task Learning for Explainable Recommendation From Figure 1, one major difference from this  post  is  Task 2 . Task 2 itself is a GRU network used to generate text explanations. Let's denote its output $\boldsymbol{o}_t$ as the distribution of corresponding words. $Y=(y_1, ..., y_T)$ the generated texts. There are two additional losses from Task 2. 1. Concept relevance loss $\mathcal{L}_c$. During training, $\mathcal{L}_c$ is used to increase the probability that the selected concepts appear in $Y$. It is computed by 2. Negative log-likelihood loss $\mathcal{L}_n$. To ensure that the generated words are similar to the ground truth ones. Plus, the original rating prediction loss: The model jointly training th

Traditional collaborative filtering methods usually only incorporate user-item rating pairs for recommendation, the vast available metadata is just ignored in such scenario. With the recent rapid developments of deep learning techniques, neural based recommendation methods is emerging. Most of them benefit from the metadata that improves personalized recommendation significantly. This  paper  is an example that is based on neural architechture for recommendation with user reviews. In this post, I just explain the model itself, for detail experiments and backgrounds, please refer to the original  paper . Problem Formulation Inputs: User ID $a$, Item ID $b$,  user $a$'s reviews set $\boldsymbol{d}_a= \{d_{a1},..., d_{al_a}\}$ and item $b$'s reviews set $\boldsymbol{d}_b= \{d_{b1},..., d_{bl_b}\}$. Note that  $\boldsymbol{d}_a$ contains all reviews given by user $a$, similarly, $\boldsymbol{d}_b$ contains all reviews received by item $b$. Outputs:  The predictred ra