Explainable recommendation has attracted much attention from the academic and industry communities. Traditional models usually leverage user reviews as ground truths for model training, and the interactions without reviews are totally ignored. However, in practice, a large amount of users may not leave reviews after purchasing items. In this paper, we argue that the interactions without reviews may also contain comprehensive user preferences, and incorporating them to build explainable recommender model may further improve the explanation quality. To follow such intuition, we first leverage generative models to predict the missing reviews, and then train the recommender model based on all the predicted and original reviews. In specific, since the reviews are discrete tokens, we regard the review generation process as a reinforcement learning problem, where each token is an action at one step. We hope that the generated reviews are indistinguishable with the real ones. Thus, we introduce an discriminator as a reward model to evaluate the quality of the generated reviews. At last, to smooth the review generation process, we introduce a self-paced learning strategy to first generate shorter reviews and then predict the longer ones. We conduct extensive experiments on three publicly available datasets to demonstrate the effectiveness of our model.

Open-source platforms such as GitHub and Stack Overflow both play significant roles in current software ecosystems. It is crucial but time-consuming for developers to raise programming questions in coding forums such as Stack Overflow and be navigated to actual solutions on GitHub repositories. In this paper, we dedicate to accelerating this activity. We find that traditional information retrieval-based methods fail to handle the long and complex questions in coding forums, and thus cannot find suitable coding repositories. To effectively and efficiently bridge the semantic gap between repositories and real-world coding questions, we introduce a specialized dataset named Repo4QA, which includes over 12,000 question-repository pairs constructed from Stack Overflow and GitHub. Furthermore, we propose QuRep, a CodeBERT-based model that jointly learns the representation of both questions and repositories. Experimental results demonstrate that our model simultaneously captures the semantic features in both questions and repositories through supervised contrastive loss and hard negative sampling. We report that our approach outperforms existing state-of-art methods by 3%-8% on MRR and 5%-8% on P@1.