As a paradigm that delves into the deep seated drivers of user behavior, motivation-based recommendation systems have emerged as a prominent research direction in the field of personalized information retrieval. Unlike traditional approaches that primarily rely on surface level interaction signals, these systems aim to uncover the intrinsic psychological factors that shape users' decision-making processes and content preferences. By modeling motivation, recommender systems can better interpret not only what users choose, but why they make such choices, thereby enhancing both the interpretability and the persuasive power of recommendations. However, existing studies often simplify motivation as a latent variable learned implicitly from behavioral data, which limits their ability to capture the semantic richness inherent in user motivations. In particular, heterogeneous information such as review texts which often carry explicit motivational cues remains underexplored in current motivation modeling frameworks. Extensive experiments conducted on three real world datasets demonstrate the effectiveness of the proposed LMMRec framework.

翻译：作为一种深入探究用户行为深层驱动因素的范式，基于动机的推荐系统已成为个性化信息检索领域的重要研究方向。与主要依赖表层交互信号的传统方法不同，这类系统旨在揭示影响用户决策过程与内容偏好的内在心理因素。通过对动机进行建模，推荐系统不仅能更好地理解用户选择的内容，更能解释其选择背后的动因，从而提升推荐的可解释性与说服力。然而，现有研究常将动机简化为从行为数据中隐式学习的潜变量，这限制了其捕捉用户动机内在语义丰富性的能力。特别是像评论文本这类常包含显性动机线索的异构信息，在当前动机建模框架中仍未得到充分探索。在三个真实世界数据集上进行的大量实验验证了所提出的LMMRec框架的有效性。