基于用户明确表达的情绪偏好，推荐能引发特定情感体验的书籍。
A Text-Based Recommender System that Leverages Explicit Affective State Preferences
- 用文本描述用户想要的情绪状态，指导推荐。
- 模型利用书评中的情绪词，匹配用户期待的情感体验。
- 适合关注情感化推荐、个性化阅读体验的研究者。
喜欢推荐内容只是情感反应的一种，还包括如着迷、好奇、因结局惊喜等更细微的情绪状态。本文提出一种新推荐任务，利用用户主动表达的丰富情绪偏好，识别出能引发这些情绪的项目。为此，我们从书评中挖掘大量细粒度情绪表达，构建了一个大规模用户偏好数据集，并设计基于Transformer的模型，以用户情绪描述和历史阅读记录为输入进行训练与评估。实验表明，能同时利用物品文本描述和用户情绪偏好信息的模型效果最佳。
原文摘要 · Abstract (English)
The affective attitude of liking a recommended item reflects just one category in a wide spectrum of affective phenomena that also includes emotions such as entranced or intrigued, moods such as cheerful or buoyant, as well as more fine-grained affective states, such as "pleasantly surprised by the conclusion". In this paper, we introduce a novel recommendation task that can leverage a virtually unbounded range of affective states sought explicitly by the user in order to identify items that, upon consumption, are likely to induce those affective states. Correspondingly, we create a large dataset of user preferences containing expressions of fine-grained affective states that are mined from book reviews, and propose a Transformer-based architecture that leverages such affective expressions as input. We then use the resulting dataset of affective states preferences, together with the linked users and their histories of book readings, ratings, and reviews, to train and evaluate multiple recommendation models on the task of matching recommended items with affective preferences. Experiments show that the best results are obtained by models that can utilize textual descriptions of items and user affective preferences.
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