Number of Distinct Values (NDV) estimation of a multiset/column is a basis for many data management tasks, especially within databases. Despite decades of research, most existing methods require either a significant amount of samples through uniform random sampling or access to the entire column to produce estimates, leading to substantial data access costs and potentially ineffective estimations in scenarios with limited data access. In this paper, we propose leveraging semantic information, i.e., schema, to address these challenges. The schema contains rich semantic information that can benefit the NDV estimation. To this end, we propose PLM4NDV, a learned method incorporating Pre-trained Language Models (PLMs) to extract semantic schema information for NDV estimation. Specifically, PLM4NDV leverages the semantics of the target column and the corresponding table to gain a comprehensive understanding of the column's meaning. By using the semantics, PLM4NDV reduces data access costs, provides accurate NDV estimation, and can even operate effectively without any data access. Extensive experiments on a large-scale real-world dataset demonstrate the superiority of PLM4NDV over baseline methods. Our code is available at https://github.com/bytedance/plm4ndv.

Virtual indexes play a crucial role in database query optimization. However, with the rapid advancement of cloud computing and AI-driven models for database optimization, traditional virtual index approaches face significant challenges. Cloud-native environments often prohibit direct conducting query optimization process on production databases due to stability requirements and data privacy concerns. Moreover, while AI models show promising progress, their integration with database systems poses challenges in system complexity, inference acceleration, and model hot updates. In this paper, we present VIDEX, a three-layer disaggregated architecture that decouples database instances, the virtual index optimizer, and algorithm services, providing standardized interfaces for AI model integration. Users can configure VIDEX by either collecting production statistics or loading from a prepared file, enabling high-accuracy what-if analysis using virtual indexes that yield query plans identical to production instances. Additionally, users can freely integrate new AI-driven algorithms into VIDEX. VIDEX has been deployed at ByteDance, serving thousands of MySQL instances daily and over millions of SQL queries for index optimization tasks.

Estimating the Number of Distinct Values (NDV) is fundamental for numerous data management tasks, especially within database applications. However, most existing works primarily focus on introducing new statistical or learned estimators, while identifying the most suitable estimator for a given scenario remains largely unexplored. Therefore, we propose AdaNDV, a learned method designed to adaptively select and fuse existing estimators to address this issue. Specifically, (1) we propose to use learned models to distinguish between overestimated and underestimated estimators and then select appropriate estimators from each category. This strategy provides a complementary perspective by integrating overestimations and underestimations for error correction, thereby improving the accuracy of NDV estimation. (2) To further integrate the estimation results, we introduce a novel fusion approach that employs a learned model to predict the weights of the selected estimators and then applies a weighted sum to merge them. By combining these strategies, the proposed AdaNDV fundamentally distinguishes itself from previous works that directly estimate NDV. Moreover, extensive experiments conducted on real-world datasets, with the number of individual columns being several orders of magnitude larger than in previous studies, demonstrate the superior performance of our method.

Session-based Recommendation (SBR) aims to predict the next item for a session, which consists of several clicked items in a transaction. Most SBR approaches follow an underlying assumption that all sequential information should be strictly utilized. Thus, they model temporal information for items using implicit, explicit, or ensemble methods. In fact, users may recall previously clicked items but might not remember the exact order in which they were clicked. Therefore, focusing on representing item temporal information in various ways could make learning session intents challenging. In this paper, we rethink the necessity of temporal information for items in SBR. We propose Aggregating the Contextual intents of the session with Attentive networks, namely ACARec. Specifically, we avoid explicitly modeling positional embeddings and learn contextual intents through aggregation methods (convolutions or poolings). We also demonstrate that even an entirely position-agnostic aggregation approach can yield promising results. Extensive experiments on real-world datasets validate our arguments. We hope our study can provide insights into SBR and inspire future research in the community.

Search result diversification aims to balance the relevance and diversity of retrieved documents to satisfy the different information needs of users. Three types of approaches have proliferated: explicit models that are based on explicit features (e.g., subtopic coverage), implicit models that are based on implicit features (e.g., the novelty of documents), and ensemble models that utilize both implicit and explicit features. However, the subtopics used by most explicit and ensemble models are usu-ally mined from queries (e.g., using Google Search Suggestions), which may not match the subtopics covered by the candidate documents. Besides, the implicit features used by most implicit models are formulated as either the similarity of documents or the intent of documents. The former cannot directly reflect the relationships of documents at the subtopic level, while the latter cannot capture non-pairwise relationships among documents. To tackle these issues, we propose a novel model that dynamically mines subtopics from the candidate documents and leverages thehypergraph structure to model the diversity of candidate documents, named HGDIV. Specifically, we dynamically mine subtopics from the candidate documents, rather than mining subtopics from queries or using static subtopics as existing methods do. More importantly, we introduce the hypergraph structure to model the diversity of candidate documents for search result diversification, which can capture the non-pairwise relationships among documents. Furthermore, we innovatively model the global and local subtopic distributions to extract the diversity of candidate documents. Experimental results on the public diversity benchmark TREC datasets demonstrate the superiority of our model over state-of-the-art models.

With the promotion of the pre-training paradigm, researchers are increasingly focusing on injecting external knowledge, such as entities and triplets from knowledge graphs, into pre-trained language models (PTMs) to improve their understanding and logical reasoning abilities. This results in significant improvements in natural language understanding and generation tasks and some level of interpretability. In this paper, we propose a novel two-stage entity knowledge enhancement pipeline for Chinese pre-trained models based on “bidirectional” prompt tuning. The pipeline consists of a “forward” stage, in which we construct fine-grained entity type prompt templates to boost PTMs injected with entity knowledge, and a “backward” stage, where the trained templates are used to generate type-constrained context-dependent negative samples for contrastive learning. Experiments on six classification tasks in the Chinese Language Understanding Evaluation (CLUE) benchmark demonstrate that our approach significantly improves upon the baseline results in most datasets, particularly those that have a strong reliance on diverse and extensive knowledge.

Session-based Recommendation (SR) aims to predict users' next click based on their behavior within a short period, which is crucial for online platforms. However, most existing SR methods somewhat ignore the fact that user preference is not necessarily strongly related to the order of interactions. Moreover, they ignore the differences in importance between different samples, which limits the model-fitting performance. To tackle these issues, we put forward the method, Mining Interest Trends and Adaptively Assigning Sample Weight, abbreviated as MTAW. Specifically, we model users' instant interest based on their present behavior and all their previous behaviors. Meanwhile, we discriminatively integrate instant interests to capture the changing trend of user interest to make more personalized recommendations. Furthermore, we devise a novel loss function that dynamically weights the samples according to their prediction difficulty in the current epoch. Extensive experimental results on two benchmark datasets demonstrate the effectiveness and superiority of our method.

Session-based Recommendation (SBR) aims to predict the next item for the current session, which consists of several clicked items in a short period by an anonymous user. Most of the sequential modeling approaches to SBR are focusing on adopting advanced Deep Neural Networks (DNNs), and these methods require increasingly longer training times. Existing studies have shown that some traditional SBR methods can outperform some DNN-based sequential models, however, few studies have attempted to investigate the effectiveness of traditional methods in recent years. In this paper, we propose a novel and concise SBR model inspired by the basic concept of autocorrelation in the Stochastic Process. Autocorrelation measures the correlation of a process at different moments. Therefore, it is natural to use it to model the correlation of clicked item sequences at different time shifts. Specifically, we use Fast Fourier Transforms (FFT) to compute the autocorrelation and combine it with several linear transformations to enhance the session representation. By this means, our proposed method can learn better session preferences and is more efficient than most DNN-based models. Extensive experiments on two public datasets show that the proposed method outperforms state-of-the-art models in both effectiveness and efficiency.

The task of Session-based Social Recommendation (SSR) aims to utilize the social networks to make recommendations in session-based scenarios. Existing SSR methods mainly focused on using graph networks to capture complex item transition patterns, ignoring the sequential information. Few studies combined two aspects of features to enhance session preferences, resulting in information loss. Besides, modeling the entire session that some items are invalid or repeatedly clicked will interfere with the results. In this paper, to address the information loss issue in SSR, we propose a novel Dual-Channel Attentive Network (DCAN) to leverage both sequential infor-mation and complex item transitions. Specifically, we construct one channel by a light graph attention layer to capture item transitions, and we elaborate a concise attention-based layer to build the other channel to learn sequential information. To solve the invalid or repeatedly clicked problem in the session, we introduce new self-supervised learning (SSL) learning method, which allows model learning to distinguish and discard these items. However, the effect of SSL in SSR has not been investigated yet. Besides, these studies require negative sampling, which makes its performance depend on negative sampling strategies. Then, we investigate the effect of adding existing SSL frameworks in DCAN, but it has not achieved good results. Besides, we propose a novel SSL framework that does not require negative sampling for SSR, denoted as Positive sampling SSL (PSSL). Furthermore, we combined DCAN and PSSL to make more accurate recommendations, denoted as DCAN - PSSL. Extensive experiments on three public benchmark datasets demonstrate that both DCAN and DCAN - PSSL consistently outperform the state-of-the-art models.