Towards a knowledge-aware food recommender system exploiting holistic user models

Cataldo Musto; Christoph Trattner; Alain D. Starke; Giovanni Semeraro

doi:10.1145/3340631.3394880

Towards a knowledge-aware food recommender system exploiting holistic user models

Cataldo Musto
, Christoph Trattner
, Alain D. Starke
, Giovanni Semeraro

Human Technology Interaction

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

40 Citations (Scopus)

Abstract

Food recommender systems typically rely on popularity, as well as similarity between recipes to generate personalized suggestions. However, this leaves little room for users to explore new preferences, such as to adopt healthier eating habits. In this short paper, we present a recommendation strategy based on knowledge about food and users' health-related characteristics to generate personalized recipes suggestions. By focusing on personal factors as a user's BMI and dietary constraints, we exploited a holistic user model to re-rank a basic recommendation list of 4,671 recipes, and investigated in a web-based experiment (N=200) to what extent it generated satisfactory food recommendations. We found that some of the information encoded in a users' holistic user profiles affected their preferences, thus providing us with interesting findings to continue this line of research.

Original language	English
Title of host publication	UMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization
Publisher	Association for Computing Machinery, Inc.
Pages	333-337
Number of pages	5
ISBN (Electronic)	9781450368612
DOIs	https://doi.org/10.1145/3340631.3394880
Publication status	Published - 7 Jul 2020
Event	28th International Conference on User Modeling, Adaptation and Personalization, UMAP 2020 - Virtual, Genoa, Italy Duration: 12 Jul 2020 → 18 Jul 2020 Conference number: 28

Conference

Conference	28th International Conference on User Modeling, Adaptation and Personalization, UMAP 2020
Abbreviated title	UMAP 2020
Country/Territory	Italy
City	Genoa
Period	12/07/20 → 18/07/20

Keywords

food recommender systems
user modeling

Access to Document

10.1145/3340631.3394880

Cite this

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title = "Towards a knowledge-aware food recommender system exploiting holistic user models",

abstract = "Food recommender systems typically rely on popularity, as well as similarity between recipes to generate personalized suggestions. However, this leaves little room for users to explore new preferences, such as to adopt healthier eating habits. In this short paper, we present a recommendation strategy based on knowledge about food and users' health-related characteristics to generate personalized recipes suggestions. By focusing on personal factors as a user's BMI and dietary constraints, we exploited a holistic user model to re-rank a basic recommendation list of 4,671 recipes, and investigated in a web-based experiment (N=200) to what extent it generated satisfactory food recommendations. We found that some of the information encoded in a users' holistic user profiles affected their preferences, thus providing us with interesting findings to continue this line of research.",

keywords = "food recommender systems, user modeling",

author = "Cataldo Musto and Christoph Trattner and Starke, \{Alain D.\} and Giovanni Semeraro",

year = "2020",

month = jul,

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doi = "10.1145/3340631.3394880",

language = "English",

pages = "333--337",

booktitle = "UMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization",

publisher = "Association for Computing Machinery, Inc.",

address = "United States",

note = "28th International Conference on User Modeling, Adaptation and Personalization, UMAP 2020, UMAP 2020 ; Conference date: 12-07-2020 Through 18-07-2020",

}

Musto, C, Trattner, C, Starke, AD & Semeraro, G 2020, Towards a knowledge-aware food recommender system exploiting holistic user models. in UMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery, Inc., pp. 333-337, 28th International Conference on User Modeling, Adaptation and Personalization, UMAP 2020, Genoa, Italy, 12/07/20. https://doi.org/10.1145/3340631.3394880

Towards a knowledge-aware food recommender system exploiting holistic user models. / Musto, Cataldo; Trattner, Christoph; Starke, Alain D. et al.
UMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery, Inc., 2020. p. 333-337.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Towards a knowledge-aware food recommender system exploiting holistic user models

AU - Musto, Cataldo

AU - Trattner, Christoph

AU - Starke, Alain D.

AU - Semeraro, Giovanni

N1 - Conference code: 28

PY - 2020/7/7

Y1 - 2020/7/7

N2 - Food recommender systems typically rely on popularity, as well as similarity between recipes to generate personalized suggestions. However, this leaves little room for users to explore new preferences, such as to adopt healthier eating habits. In this short paper, we present a recommendation strategy based on knowledge about food and users' health-related characteristics to generate personalized recipes suggestions. By focusing on personal factors as a user's BMI and dietary constraints, we exploited a holistic user model to re-rank a basic recommendation list of 4,671 recipes, and investigated in a web-based experiment (N=200) to what extent it generated satisfactory food recommendations. We found that some of the information encoded in a users' holistic user profiles affected their preferences, thus providing us with interesting findings to continue this line of research.

AB - Food recommender systems typically rely on popularity, as well as similarity between recipes to generate personalized suggestions. However, this leaves little room for users to explore new preferences, such as to adopt healthier eating habits. In this short paper, we present a recommendation strategy based on knowledge about food and users' health-related characteristics to generate personalized recipes suggestions. By focusing on personal factors as a user's BMI and dietary constraints, we exploited a holistic user model to re-rank a basic recommendation list of 4,671 recipes, and investigated in a web-based experiment (N=200) to what extent it generated satisfactory food recommendations. We found that some of the information encoded in a users' holistic user profiles affected their preferences, thus providing us with interesting findings to continue this line of research.

KW - food recommender systems

KW - user modeling

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DO - 10.1145/3340631.3394880

M3 - Conference contribution

SP - 333

EP - 337

BT - UMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

PB - Association for Computing Machinery, Inc.

T2 - 28th International Conference on User Modeling, Adaptation and Personalization, UMAP 2020

Y2 - 12 July 2020 through 18 July 2020

ER -

Towards a knowledge-aware food recommender system exploiting holistic user models

Abstract

Conference

Keywords

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