Causal self-attention models such as SASRec are widely used in sequential recommendation, where learned attention weights are often assumed to provide crucial importance weighting over past interactions. Yet it is unclear when predictive performance truly depends on such non-uniform weighting. We study a controlled SASRec variant that replaces learned attention weights with uniform aggregation and is trained under an otherwise identical block structure and training recipe. Across fourteen benchmark datasets, this modification often yields performance comparable to the original model, with clear dataset-dependent exceptions. To explain this heterogeneity, we introduce a stage-wise norm-based decomposition that quantifies self-preserving vs. cross-position mixing within attention blocks. Across datasets, we find distinct regimes: low mixing yields robustness to uniformization; higher mixing tends to coincide with sensitivity, while some datasets exhibit substantial mixing without dependence on learned weighting. Our results provide a practical diagnostic for identifying when attention weighting is functionally utilized in sequential recommendation. The code is available at: https://github.com/keito0329/revisiting-sasrec.

A financial recommender system couples two objectives: ranking for preference alignment so that users actually adopt the recommendations, and ranking for outcome quality so that adoption translates into value. These objectives can conflict: return-driven lists may narrow diversification and miss user tastes, while relevance-only lists deliver weak realized returns. To address these problems, we propose Risk-aware Utility re-RAnking (RURA), a plug-in method that operates on the upstream top candidates and optimizes a user-specific expected-utility objective. RURA injects investor risk tolerance into the utility, includes a likelihood-aware variant that integrates calibrated adoption probabilities, and uses a single hyperparameter to control diversification to preserve upstream order while trading minimal nDCG loss for ROI gains. Experiments on a real-world dataset demonstrate that RURA outperforms risk-aware baselines in ROI while keeping nDCG within the range of a strong risk-aware baseline and delivering higher expected utility across risk groups.

Multimedia content recommendation needs to consider users' preferences for each content. Conventional recommender systems consider them with wearable sensors, however, wearing such sensors can lead to a burden on users. In this paper, we construct a recommender system that can explicitly estimate users' preferences without wearable sensors. Specifically, by constructing lightweight but strong machine learning models suitable for our system, the users' interest levels for contents can be estimated from facial images obtained from a widely used webcam. In addition, through the interaction that the user selects displayed contents, our system finds the tendency of personal preferences for recommending contents with high user satisfaction. Our system is available on https://www.lmd-demo.org/2022/start_eng.html.