User-item interaction histories are pivotal for sequential recommendation systems but often include noise, such as unintended clicks or actions that fail to reflect genuine user preferences. To address this, we propose Learned Item Shortcuts for Sequential Recommendation (LISRec), a novel framework that explicitly captures stable preferences by extracting personalized semantic shortcuts from historical interactions. LISRec first learns task-agnostic semantic representations to assess item similarities, then constructs a personalized semantic graph over all user-interacted items. By identifying the maximal semantic connectivity subset within this graph, LISRec selects the most representative items as semantic shortcuts to guide user preference modeling. This focused representation filters out irrelevant actions while preserving the diversity of genuine interests. Experimental results on the Yelp and Amazon Product datasets illustrate that LISRec achieves a 13% improvement over baseline recommendation models, showing its effectiveness in capturing stable user interests. Further analysis indicates that shortcut-based histories better capture user preferences, making more accurate and relevant recommendations. All codes and datasets are available at https://github.com/NEUIR/LISRec.

Audio Description (AD) aims to generate narrations of information that is not accessible through unimodal hearing in movies to aid the visually impaired in following film narratives. Current solutions rely heavily on manual work, resulting in high costs and limited scalability. While automatic methods have been introduced, they often yield descriptions that are sparse and omit key details. ddressing these challenges, we propose a novel automated pipeline, the Multi-modal Movie Audio Description (MMAD). MMAD harnesses the capabilities of three key modules as well as the power of Llama2 to augment the depth and breadth of the generated descriptions. Specifically, first, we propose an Audio-aware Feature Enhancing Module to provide the model with multi-modal perception capabilities, enriching the background descriptions with a more comprehensive understanding of the environmental features. Second, we propose an Actor-tracking-aware Story Linking Module to aid in the generation of contextual and character-centric descriptions, thereby enhancing the richness of character depictions. Third, we incorporate a Subtitled Movie Clip Contextual Alignment Module, supplying semantic information about various time periods throughout the movie, which facilitates the consideration of the full movie narrative context when describing silent segments, thereby enhancing the richness of the descriptions. Experiments on widely used datasets convincingly demonstrates that MMAD significantly surpasses existing strong baselines in performance, establishing a new state-of-the-art in the field. Our code will be released at https://github.com/Daria8976/MMAD.