Department of Computer Science, North Dakota State University, Fargo, ND 58105, USA.
Nutrients. 2024 Jan 24;16(3):346. doi: 10.3390/nu16030346.
Eating, central to human existence, is influenced by a myriad of factors, including nutrition, health, personal taste, cultural background, and flavor preferences. The challenge of devising personalized meal plans that effectively encompass these dimensions is formidable. A crucial shortfall in many existing meal-planning systems is poor user adherence, often stemming from a disconnect between the plan and the user's lifestyle, preferences, or unseen eating patterns. Our study introduces a pioneering algorithm, CFRL, which melds reinforcement learning (RL) with collaborative filtering (CF) in a unique synergy. This algorithm not only addresses nutritional and health considerations but also dynamically adapts to and uncovers latent user eating habits, thereby significantly enhancing user acceptance and adherence. CFRL utilizes Markov decision processes (MDPs) for interactive meal recommendations and incorporates a CF-based MDP framework to align with broader user preferences, translated into a shared latent vector space. Central to CFRL is its innovative reward-shaping mechanism, rooted in multi-criteria decision-making that includes user ratings, preferences, and nutritional data. This results in versatile, user-specific meal plans. Our comparative analysis with four baseline methods showcases CFRL's superior performance in key metrics like user satisfaction and nutritional adequacy. This research underscores the effectiveness of combining RL and CF in personalized meal planning, marking a substantial advancement over traditional approaches.
进食是人类生存的核心，受到众多因素的影响，包括营养、健康、个人口味、文化背景和风味偏好。设计能够有效涵盖这些维度的个性化膳食计划是一项艰巨的挑战。许多现有膳食计划系统的一个关键缺陷是用户的依从性差，这通常源于计划与用户的生活方式、偏好或未被发现的饮食习惯之间的脱节。我们的研究引入了一种开创性的算法 CFRL，它将强化学习（RL）与协同过滤（CF）融合在一起，形成了独特的协同作用。该算法不仅考虑了营养和健康因素，还能够动态适应和揭示潜在的用户饮食习惯，从而显著提高用户的接受度和依从性。CFRL 使用马尔可夫决策过程（MDP）进行交互式膳食推荐，并采用基于 CF 的 MDP 框架来适应更广泛的用户偏好，这些偏好被转化为共享的潜在向量空间。CFRL 的核心是其创新的奖励塑造机制，该机制基于多准则决策，包括用户评分、偏好和营养数据。这导致了多样化的、针对用户的膳食计划。我们与四种基线方法的比较分析展示了 CFRL 在用户满意度和营养充足性等关键指标上的优越性能。这项研究强调了在个性化膳食计划中结合 RL 和 CF 的有效性，标志着传统方法的重大进展。
