The traditional recommendation framework seeks to connect user and content, by finding the best match possible based on users past interaction. However, a good content recommendation is not necessarily similar to what the user has chosen in the past. As humans, users naturally evolve, learn, forget, get bored, they change their perspective of the world and in consequence, of the recommendable content. One well known mechanism that affects user interest is the Mere Exposure Effect: when repeatedly exposed to stimuli, users' interest tends to rise with the initial exposures, reaching a peak, and gradually decreasing thereafter, resulting in an inverted-U shape. Since previous research has shown that the magnitude of the effect depends on a number of interesting factors such as stimulus complexity and familiarity, leveraging this effect is a way to not only improve repeated recommendation but to gain a more in-depth understanding of both users and stimuli. In this work we present (Mere) Exposure2Vec (Ex2Vec) our model that leverages the Mere Exposure Effect in repeat consumption to derive user and item characterization and track user interest evolution. We validate our model through predicting future music consumption based on repetition and discuss its implications for recommendation scenarios where repetition is common.

翻译：传统推荐框架通过用户过往交互寻找最佳匹配，以连接用户与内容。然而，优质内容推荐未必与用户历史选择相似。作为人类，用户会自然演化、学习、遗忘、产生倦怠，其世界观随之改变，进而影响其对可推荐内容的认知。一个广为人知的影响用户兴趣的机制是纯粹曝光效应：当反复接触同一刺激时，用户兴趣会随初始曝光次数增加而上升，达到峰值后逐渐下降，呈现倒U型曲线。由于已有研究证实该效应强度受刺激复杂度、熟悉度等多重因素影响，利用此效应不仅能优化重复推荐，更能深入理解用户与刺激物的本质。本文提出（纯粹）曝光向量化（Ex2Vec）模型，通过重复消费场景中的纯粹曝光效应，实现用户与物品表征的构建及用户兴趣演变追踪。我们通过基于重复行为的音乐消费预测验证模型有效性，并探讨其对重复推荐场景的启示。