Recent advances in large language models have led to the widespread integration of chatbots as built-in features in modern applications. To model user latent intent and provide topic guidance, the question recommendation task serves as the entry point for built-in chatbots. In content-consuming applications, interaction data with built-in chatbots exhibit notable sparsity, while consuming signals account for the majority of user behaviors. Therefore, it is reasonable to incorporate content-consuming signals for user preference modeling. However, a significant domain shift exists between the preference in the content-consuming domain and that in the conversational topic domain. It is non-trivial to transfer those cross-domain signals into meaningful suggested topics that are aligned with users' real-time preference.

Large Language Model (LLM)-driven conversational search is shifting information retrieval from reactive keyword matching to proactive, open-ended dialogues. In this context, Conversation Starters are widely deployed to provide personalized query recommendations that help users initiate dialogues. Conventionally, recommending these starters relies on a closed ''exposure-click'' loop. Yet, this feedback loop mechanism traps the system in an echo chamber where, compounded by data sparsity, it fails to capture the dynamic nature of conversational search intents shaped by the open world. As a result, the system skews towards popular but generic suggestions. In this work, we uncover an untapped paradigm shift to shatter this harmful feedback loop: harnessing user ''free will'' through active user expressions. Unlike traditional recommendations, conversational search empowers users to bypass menus entirely through manually typed queries. The open-world intents in active queries hold the key to breaking this loop. However, incorporating them is non-trivial: (1) there exists an inherent distribution shift between active queries and formulated starters. (2) Furthermore, the ''non-ID-able'' nature of open text renders traditional item-based popularity statistics ineffective for large-scale industrial streaming training. To this end, we propose Passive-Active Bridge (PA-Bridge), a novel framework that employs an adversarial distribution aligner to bridge the distributional gap between passively recommended starters and active expressions. Moreover, we introduce a semantic discretizer to enable the deployment of popularity debiasing algorithms. Online A/B tests on our platform, which serves hundreds of millions of users, demonstrate that PA-Bridge significantly boosts the Feature Penetration Rate by 0.54% and User Active Days by 0.04%.

Recently, motivated by the outstanding achievements of diffusion models, the diffusion process has been employed to strengthen representation learning in recommendation systems. Most diffusion-based recommendation models typically utilize standard Gaussian noise in symmetric forward and reverse processes in continuous data space. Nevertheless, the samples derived from recommendation systems inhabit a discrete data space, which is fundamentally different from the continuous one. Moreover, Gaussian noise has the potential to corrupt personalized information within latent representations. In this work, we propose a novel and effective method, named Asymmetric Diffusion Recommendation Model (AsymDiffRec), which learns forward and reverse processes in an asymmetric manner. We define a generalized forward process that simulates the missing features in real-world recommendation samples. The reverse process is then performed in an asymmetric latent feature space. To preserve personalized information within the latent representation, a task-oriented optimization strategy is introduced. In the serving stage, the raw sample with missing features is regarded as a noisy input to generate a denoising and robust representation for the final prediction. By equipping base models with AsymDiffRec, we conduct online A/B tests, achieving improvements of +0.131% and +0.166% in terms of users' active days and app usage duration respectively. Additionally, the extended offline experiments also demonstrate improvements. AsymDiffRec has been implemented in the Douyin Music App.

The rise of LLM has enabled natural language-based table assistants, but existing systems assume users already have a well-formed table, neglecting the challenge of table discovery in large-scale table pools. To address this, we introduce TableCopilot, an LLM-powered assistant for interactive, precise, and personalized table discovery and analysis. We define a novel scenario, nlcTD, where users provide both a natural language condition and a query table, enabling intuitive and flexible table discovery for users of all expertise levels. To handle this, we propose Crofuma, a cross-fusion-based approach that learns and aggregates single-modal and cross-modal matching scores. Experimental results show Crofuma outperforms SOTA single-input methods by at least 12% on NDCG@5. We also release an instructional video, codebase, datasets, and other resources on GitHub to encourage community contributions. TableCopilot sets a new standard for interactive table assistants, making advanced table discovery accessible and integrated.

Among numerous explainable AI (XAI) methods proposed in recent years, model explanations based on Shapley values are widely accepted for their solid theoretical support from game theory. However, most existing methods approximate Shapley values based on a feature independence assumption considering the complexity of calculating exact Shapley values. This assumption could bring some counterfactual problems when interpreted features are highly correlated and result in explanations contrary to human intuition. In this paper, we propose PriSHAP to explicitly model the dependency relationship between correlated features and provide reasonable explanations for tabular data. Feature dependencies are analyzed and taken as prior information to guide the process of estimating Shapley values. Additionally, PriSHAP is free to be applied in popular Shapley value-based explainers to address counterfactual problems while providing more faithful explanations. A pipeline is given to apply PriSHAP in existing explainers with simple adjustments. Extensive experiments on both public datasets and artificial datasets are provided to demonstrate the effectiveness of our method.