Food choices are personal and complex and have a significant impact on our
long-term health and quality of life. By helping users to make informed and
satisfying decisions, Recommender Systems (RS) have the potential to support
users in making healthier food choices. Intelligent users-modeling is a key
challenge in achieving this potential. This paper investigates Ensemble Topic
Modelling (EnsTM) based Feature Identification techniques for efficient
user-modeling and recipe recommendation. It builds on findings in EnsTM to
propose a reduced data representation format and a smart user-modeling strategy
that makes capturing user-preference fast, efficient and interactive. This
approach enables personalization, even in a cold-start scenario. This paper
proposes two different EnsTM based and one Hybrid EnsTM based recommenders. We
compared all three EnsTM based variations through a user study with 48
participants, using a large-scale,real-world corpus of 230,876 recipes, and
compare against a conventional Content Based (CB) approach. EnsTM based
recommenders performed significantly better than the CB approach. Besides
acknowledging multi-domain contents such as taste, demographics and cost, our
proposed approach also considers user's nutritional preference and assists them
finding recipes under diverse nutritional categories. Furthermore, it provides
excellent coverage and enables implicit understanding of user's food practices.
Subsequent analysis also exposed correlation between certain features and a
healthier lifestyle.

本论文探讨了基于 Ensemble Topic Modelling（EnsTM）的特征识别技术在用户建模和食谱推荐方面的有效性，并提出了一种智能用户建模策略，该策略使捕捉用户偏好变得快速、高效和交互式，提出的 EnsTM 方法在 240,876 个食谱上与传统的基于内容的（CB）方法进行了比较，通过 48 个参与者的用户研究表明效果显著，具备个性化推荐的能力。