Modern recommender systems aim to identify items that are most pertinent to a particular user and are particularly useful when an overwhelming number of items are present. Feature embedding is essential to deep recommender systems, which constructs memory-efficient and semantically meaningful representations by mapping high-dimensional sparse feature vectors into low-dimensional dense vectors. Most existing systems assign a unified dimension to all feature fields, regardless of the diverse importance of different features, which usually results in sub-optimal performance and high memory usage. In this paper, we propose a low-cost embedding dimension search approach named DimReg for recommender systems, by assessing information overlapping between the dimensions within each feature field and pruning unimportant and redundant dimensions progressively during model training via a two-level polarization regularizer, while introducing minimum overhead. Moreover, our method does not require retraining after embedding dimension search, which significantly reduces the computational cost and is more friendly to deployment in real-world recommender systems. Extensive experiments conducted on multiple CTR (Click Through Rate) prediction tasks demonstrate that our method can efficiently reduce the model parameters up to 98.6%, and achieve strong recommendation performance outperforming existing automated embedding dimension search methods.

AutoAugment is an automatic method to design data augmentation policies for deep learning, and has achieved significant improvements on computer vision tasks. However, since early AutoAugment approaches cost thousands of GPU hours, there is a recent demand to investigate low-cost search methods that can still find effective augmentation policies. In this paper, we propose a multi-armed bandit algorithm, named Bandit Data Augment (BDA), to efficiently search for optimal and transferable data augmentation policies. We leverage Successive Halving to make the bandit model progressively focus on more promising augmentation operations during the search, leading to sparse selection of operations and more generalizable augmentation policies. We also propose a computationally efficient rewarding scheme to reduce the evaluation cost of augmentation policies. Extensive experiments demonstrate that BDA can achieve comparable or better performance than prior Auto Augment methods on a wide range of models on CIFAR-10/100 and ImageNet benchmarks. Besides, BDA is 555 times and 536 times faster than AutoAugment on CIFAR-10 and ImageNet, respectively. In addition, BDA is 16 times faster than Fast Auto Augment on ImageNet. More importantly, BDA can discover policies that are transferable across datasets and models, and achieve similar performance to policies found directly on the target dataset.

Text clustering, as one of the most fundamental challenges in unsupervised learning, aims at grouping semantically similar text segments without relying on human annotations. With the rapid development of deep learning, deep clustering has achieved significant advantages over traditional clustering methods. Despite the effectiveness, most existing deep text clustering methods rely heavily on representations pre-trained in general domains, which may not be the most suitable solution for clustering in specific target domains. To address this issue, we propose CEIL, a novel Classification-Enhanced Iterative Learning framework for short text clustering, which aims at generally promoting the clustering performance by introducing a classification objective to iteratively improve feature representations. In each iteration, we first adopt a language model to retrieve the initial text representations, from which the clustering results are collected using our proposed Category Disentangled Contrastive Clustering (CDCC) algorithm. After strict data filtering and aggregation processes, samples with clean category labels are retrieved, which serve as supervision information to update the language model with the classification objective via a prompt learning approach. Finally, the updated language model with improved representation ability is used to enhance clustering in the next iteration. Extensive experiments demonstrate that the CEIL framework significantly improves the clustering performance over iterations, and is generally effective on various clustering algorithms. Moreover, by incorporating CEIL on CDCC, we achieve the state-of-the-art clustering performance on a wide range of short text clustering benchmarks outperforming other strong baseline methods.

Cross-domain recommendation can help alleviate the data sparsity issue in traditional sequential recommender systems. In this paper, we propose the RecGURU algorithm framework to generate a Generalized User Representation (GUR) incorporating user information across domains in sequential recommendation, even when there is minimum or no common users in the two domains. We propose a self-attentive autoencoder to derive latent user representations, and a domain discriminator, which aims to predict the origin domain of a generated latent representation. We propose a novel adversarial learning method to train the two modules to unify user embeddings generated from different domains into a single global GUR for each user. The learned GUR captures the overall preferences and characteristics of a user and thus can be used to augment the behavior data and improve recommendations in any single domain in which the user is involved. Extensive experiments have been conducted on two public cross-domain recommendation datasets as well as a large dataset collected from real-world applications. The results demonstrate that RecGURU boosts performance and outperforms various state-of-the-art sequential recommendation and cross-domain recommendation methods. The collected data will be released to facilitate future research.

Automatic question generation is an important technique that can improve the training of question answering, help chatbots to start or continue a conversation with humans, and provide assessment materials for educational purposes. Existing neural question generation models are not sufficient mainly due to their inability to properly model the process of how each word in the question is selected, i.e., whether repeating the given passage or being generated from a vocabulary. In this paper, we propose our Clue Guided Copy Network for Question Generation (CGC-QG), which is a sequence-to-sequence generative model with copying mechanism, yet employing a variety of novel components and techniques to boost the performance of question generation. In CGC-QG, we design a multi-task labeling strategy to identify whether a question word should be copied from the input passage or be generated instead, guiding the model to learn the accurate boundaries between copying and generation. Furthermore, our input passage encoder takes as input, among a diverse range of other features, the prediction made by a clue word predictor, which helps identify whether each word in the input passage is a potential clue to be copied into the target question. The clue word predictor is designed based on a novel application of Graph Convolutional Networks onto a syntactic dependency tree representation of each passage, thus being able to predict clue words only based on their context in the passage and their relative positions to the answer in the tree. We jointly train the clue prediction as well as question generation with multi-task learning and a number of practical strategies to reduce the complexity. Extensive evaluations show that our model significantly improves the performance of question generation and out-performs all previous state-of-the-art neural question generation models by a substantial margin.