Personalized, Health-Aware Recipe Recommendation: An Ensemble Topic Modeling Based Approach

TL;DR

This paper presents an ensemble topic modeling approach for personalized, health-aware recipe recommendation, improving user modeling efficiency and recommendation quality, especially in cold-start scenarios, by leveraging multi-domain content and nutritional preferences.

Contribution

It introduces novel EnsTM-based recommenders and a hybrid model that enhance personalization and health-awareness in recipe recommendations, outperforming traditional content-based methods.

Findings

EnsTM recommenders outperform conventional CB approaches.

The approach effectively incorporates nutritional preferences.

Features correlate with healthier lifestyles.

Abstract

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.