Given a user's risk preference and historical data, how can we generate diverse and high-quality portfolios for risk-aware portfolio optimization? Portfolio optimization is a core financial problem that balances returns and risks by determining asset allocations. While deterministic deep learning approaches can be directly optimized for a single solution, they offer limited flexibility to handle users' risk preferences. In contrast, stochastic methods typically rely on multi-stage processes, which complicate training and do not strictly align with the ultimate goal of generating optimal portfolios. In this paper, we propose Diffolio (Diffusion Models for Risk-Aware Portfolio Optimization), a novel diffusion-based framework that directly learns a pseudo-optimal portfolio distribution, addressing the drawbacks of (i) deterministic models lacking flexibility and (ii) stochastic models that are complex and misaligned with the true portfolio optimization objective. Instead of forecasting entire future time series, Diffolio directly samples portfolios, immediately adapting to user-specified risk levels through a dedicated risk guidance mechanism embedded in the denoising diffusion process. Empirical results on multiple real-world market datasets show that Diffolio significantly outperforms existing baselines in terms of return, risk control, and overall reliability. In particular, Diffolio achieves up to 12.1%p higher Annualized Rate of Return, demonstrating its strong potential as a risk-aware and objective-oriented solution to portfolio optimization.

Given a user query containing ambiguous and user-specific references, how can we effectively retrieve personalized information? Personalized information retrieval (PIR) requires resolving context-dependent cues --- such as nicknames, personal locations, or temporal expressions. This poses challenges for conventional retrievers, including dense and generative models, which often struggle with entity ambiguity and generalization to user-specific contexts. In this paper, we propose PEARL (Personalized Entity-Aware Generative RetrievaL), a novel generative retrieval framework for personalized IR. PEARL addresses key challenges through three components: (i) entity-aware annotation with span-level regularization to reduce lexical sensitivity, (ii) prefix-based contrastive learning to capture structural alignment between lexically divergent query-passage pairs, and (iii) context diversification to improve robustness against user-specific variations. Empirical results on both an existing PIR dataset and our new large-scale synthetic benchmark PAIR show that PEARL consistently outperforms strong baselines under zero-shot evaluation. Notably, PEARL achieves the state-of-the-art performance in Hits@1 and MRR@10, demonstrating its effectiveness for retrieval in personalized user contexts. Our dataset is available at https://www.github.com/pearl-pair/pearl.

How can we address distribution shifts in stock price data to improve stock price prediction accuracy? Stock price prediction has attracted attention from both academia and industry, driven by its potential to uncover complex market patterns and enhance decisionmaking. However, existing methods often fail to handle distribution shifts effectively, focusing on scaling or representation adaptation without fully addressing distributional discrepancies and shape misalignments between training and test data.

How can we leverage inherent frequency features of stock signals for effective portfolio optimization? Portfolio optimization in the domain of finance revolves around strategically allocating assets to maximize returns. Recent advancements highlight the efficacy of deep learning and reinforcement learning (RL) in capturing temporal asset patterns for portfolio optimization. However, previous methodologies focusing on time-domain often fail to detect sudden market shifts and abrupt events because their models are overly tailored to prevalent patterns, resulting in significant losses.

Given historical stock prices and sparse tweets, how can we accurately predict stock price movement? Many market analysts strive to use a large amount of information for stock price prediction, and Twitter is one of the richest sources of information presenting real-time opinions of people. However, previous works that use tweet data in stock movement prediction have suffered from two limitations. First, the number of tweets is heavily biased towards only a few popular stocks, and most stocks have insufficient evidence for accurate price prediction. Second, many tweets provide noisy information irrelevant of actual price movement, and extracting reliable information from tweets is as challenging as predicting stock prices.In this paper, we propose SLOT (Self-supervised Learning of Tweets for Capturing Multi-level Price Trends), an accurate method for stock movement prediction. SLOT has two main ideas to address the limitations of previous tweet-based models. First, SLOT learns embedding vectors of stocks and tweets in the same semantic space through self-supervised learning. The embeddings allow us to use all available tweets to improve the prediction for even unpopular stocks, addressing the sparsity problem. Second, SLOT learns multi-level relationships between stocks from tweets, rather than using them as direct evidence for prediction, making it robust to the unreliability of tweets. Extensive experiments on real world datasets show that SLOT provides the state-of-the-art accuracy of stock movement prediction.