C$^2$-Rec: An Effective Consistency Constraint for Sequential Recommendation

Sequential recommendation methods play an important role in real-world recommender systems. These systems are able to catch user preferences by taking advantage of historical records and then performing recommendations. Contrastive learning(CL) is a cutting-edge technology that can assist us in obtaining informative user representations, but these CL-based models need subtle negative sampling strategies, tedious data augmentation methods, and heavy hyper-parameters tuning work. In this paper, we introduce another way to generate better user representations and recommend more attractive items to users. Particularly, we put forward an effective \textbf{C}onsistency \textbf{C}onstraint for sequential \textbf{Rec}ommendation(C$^2$-Rec) in which only two extra training objectives are used without any structural modifications and data augmentation strategies. Substantial experiments have been conducted on three benchmark datasets and one real industrial dataset, which proves that our proposed method outperforms SOTA models substantially. Furthermore, our method needs much less training time than those CL-based models. Online AB-test on real-world recommendation systems also achieves 10.141\% improvement on the click-through rate and 10.541\% increase on the average click number per capita. The code is available at \url{https://github.com/zhengrongqin/C2-Rec}.

R-Drop: Regularized Dropout for Neural Networks

Dropout is a powerful and widely used technique to regularize the training of deep neural networks. In this paper, we introduce a simple regularization strategy upon dropout in model training, namely R-Drop, which forces the output distributions of different sub models generated by dropout to be consistent with each other. Specifically, for each training sample, R-Drop minimizes the bidirectional KL-divergence between the output distributions of two sub models sampled by dropout. Theoretical analysis reveals that R-Drop reduces the freedom of the model parameters and complements dropout. Experiments on $\bf{5}$ widely used deep learning tasks ($\bf{18}$ datasets in total), including neural machine translation, abstractive summarization, language understanding, language modeling, and image classification, show that R-Drop is universally effective. In particular, it yields substantial improvements when applied to fine-tune large-scale pre-trained models, e.g., ViT, RoBERTa-large, and BART, and achieves state-of-the-art (SOTA) performances with the vanilla Transformer model on WMT14 English$\to$German translation ($\bf{30.91}$ BLEU) and WMT14 English$\to$French translation ($\bf{43.95}$ BLEU), even surpassing models trained with extra large-scale data and expert-designed advanced variants of Transformer models. Our code is available at GitHub{\url{https://github.com/dropreg/R-Drop}}.

TechKG: A Large-Scale Chinese Technology-Oriented Knowledge Graph

Knowledge graph is a kind of valuable knowledge base which would benefit lots of AI-related applications. Up to now, lots of large-scale knowledge graphs have been built. However, most of them are non-Chinese and designed for general purpose. In this work, we introduce TechKG, a large scale Chinese knowledge graph that is technology-oriented. It is built automatically from massive technical papers that are published in Chinese academic journals of different research domains. Some carefully designed heuristic rules are used to extract high quality entities and relations. Totally, it comprises of over 260 million triplets that are built upon more than 52 million entities which come from 38 research domains. Our preliminary ex-periments indicate that TechKG has high adaptability and can be used as a dataset for many diverse AI-related applications. We released TechKG at: http://www.techkg.cn.