Internationalof Computational
Intelligence
Systems
Journal ofJournal
Robotics,
Networking andArtificial Life
Vol.
XX(Z);
Month(2018),(Year), pp.
pp. 204–207
xx–yy
Vol.
5(3);
December
DOI:
10.1080/XXXXXXXXXXXXXX;ISSN XXXX–XXXX
onlineDOI:
10.2991/jrnal.2018.5.3.13;
ISSN 2405-9021
print; 2352-6386
onlinehttps://www.atlantis-press.com/journals/ijndc
https://www.atlantis-press.com/journals/jrnal

Research Article

A Recipe Decision Support System Using Knowledge
Information and Agent
Keita Saito1, Taro Asada2, Yasunari Yoshitomi2,*, Ryota Kato2, Masayoshi Tabuse2
Food IT System Division, JFE Systems, Inc., Sumida-ku, Tokyo 130-0012, Japan
Graduate School of Life and Environmental Sciences, Kyoto Prefectural University, Sakyo-ku, Kyoto 606-8522, Japan

12ARTICLE INFO
Article History
Received 22 May 2018
Accepted 21 November 2018
Keywords

ABSTRACTWe report on the development of a recipe recommendation system using collaborative filtering and impression words. As auser interface (UI), we adopted MikuMikuDanceAgent (MMDAgent). In our system, the first recommendation process using
collaborative filtering is terminated based on the previously decided condition, after which the second recommendation process
identifies the recipe that is most similar to past successful recommendations from recipes that have not been recommended thusfar. As a final step, one recipe is selected from all successfully recommended recipes.

Recipe recommendation
collaborative filtering
impression word
MMDAgent© 2018 The Authors. Published by Atlantis Press SARL.
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
It is sometimes bothersome to decide upon a recipe for meals
because there are frequently numerous choices available.
Therefore, a support system that recommends a new recipe every
day could be beneficial. In this study, we developed and evaluateda new system that can be used to narrow down recipe choices toa final recommendation. In the current study, we have adopted
MikuMikuDanceAgent (MMDAgent) [1] as a user interface (UI).
In this study, we propose a system that narrows down recipe
choices to a final recommendation using a method that considers
yesterday’s dinner recipe.

2. RECIPE RECOMMENDATIONMETHOD USING COLLABORATIVEFILTERING
The recipe recommendation method [2] proposed in our current
study is based on collaborative filtering, and is, in turn, based on
our previously reported music recommendation method [3]. Thatmethod is explained briefly below.
We began by preparing a database composed of recipes that had
been already been subjectively scored at five-levels with “5” being
the most appealing to “1” being the least appealing. We then transformed the subjective scores of “1–3” and “4–5” into “0” and “1”,respectively. Those scores (“0” or “1”) are expressed as “evaluation
scores” below.

* Corresponding author. Email: yoshitomi@kpu.ac.jp

3. RECIPE RECOMMENDATION METHODUSING IMPRESSION WORDS
Our recipe recommendation method also uses impression words[2]. In this study, 10 impression words pairs (Table 1) extracted
from “Sizzle Word Report 2014” [4] were used. The method
explained briefly below is also based on our previously reported
music recommendation method [3].
Each recipe prepared in advance was assigned one of seven scorelevels ranging from “−3” to “+3” by participating test subjects whoused those impression word pairs. We then transformed these subjective scores to a three-level scale (“−3” to “−2” as “−1”, “−1” to“+1” as “0” and “+2” to “+3” as “+1”). These scores are expressed as“impression evaluation scores” below.
When a recipe not recommended to a user has the same values
except “0” as that for at least one recommended recipe having a highevaluation by the user on the three-level score for at least five
impression words, the recipe is treated as having a positive evaluation by the user. In contrast, when a recipe not recommended to theuser has the same scores (disregarding “0”) as that for another recipe
just recommended to the user and having a negative evaluation by
the user on the three-level score for at least seven impression words,
the recipe is treated as having a negative evaluation by the user.

4. RECIPE RECOMMENDATION USINGTHE COLLABORATIVE FILTERING AND
IMPRESSION WORDS
Figure 1 shows a flowchart of the proposed system, which combines
collaborative filtering and impression words based on the authors’K. Saito et al. / Journal of Robotics, Networking and Artificial Life 5(3) 204–207

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Table 1 | Impression word pairs
Good flavor
Mellow
Sweet
Addicted
RichJuicy
WarmMelty
Seasonal limited
Authentic

Refreshing flavor
Spicy
GentleNot addicted
CrispyScratchy
Cool
Chewy
Not seasonal limited
Not authentic

recipe recommendation method [2]. The system recommends recipes stored in the database to the user. Both the recommendation
process using collaborative filtering and impression words are terminated when the number of recommended recipes reaches the pre-setupper limit (K). In our proposed system, the recommendation process using collaborative filtering is terminated when there is no users
in the user reference list that show the exact same evaluation for therecommended recipe as the user up to that point. The recommendation process then continues by identifying the most similar recipe,from the viewpoints of three-level scores (disregarding “0”) based on
impression words, to that successfully recommended among recipes
not yet recommended. Just before concluding the recommendation
process, the user database is updated with subjective evaluations of
the recipe by adding the viewpoints of users the recipe by adding theviewpoints of users recommending the recipe.

5. PROPOSED SYSTEM
In this section, we propose a system that narrows down recipe
choices to a final recommendation using a method that considers yesterday’s dinner recipe in addition to the recommendation
method discussed in Section 4. First, recipes in recipe groups thatare similar to the recipe that user selected the day before are recommended by using the method described in Section 4. Next, recipes
in recipe groups that differ from previous day’s selection are also
recommended by using the method described in Section 4. As in
the impression word recommendation, the judgment of a recipe
whose impressions are similar to yesterday’s dinner recipe is based
whether the “impression evaluation score” coincides with five or
more of the impression word pairs of 10 items. Figure 2 shows arecipe recommendation flowchart that considers yesterday’s dinner.
The flowchart of “System CI” in Figure 2 is described in Figure 1.

6. PERFORMANCE EVALUATIONS
6.1. Conditions
From a total of 49 recipes that exclude side dishes, soups, andarranged dishes, a main course dish that one or more user designated as “I have never tried” were extracted from the 293 entries
in the “Rakuten recipe” [5] category for use in our evaluation. The
impression words selected were four pairs of “taste expression”, four
pairs of “texture expression”, and two pairs of “situation expression”
extracted from the “Sizzle Word Report 2014” [4]. Synonyms andantonyms were judged in the Weblio dictionary [6]. Similar expressions in the “expression” rankings are grouped together and chosen
via the following procedure:

Figure 1 | Recipe recommendation using collaborative filtering andimpression words. ARDB, set of all recipe in database; RCL, set of recipe
in recommendation list; AUSE, set of all users with subjective estimation;
UL, set of user in reference user list; NRR, number of recommended recipe;
UEC, set of user(s) who estimate(s) recommended recipe Rr contrary touser u; RSNPRIW, set of recipe having similarity to at least one recipe inARDB ∩ PRL on impression words; PRL, set of recipe having 3.5 or higher
than 3.5 of score given by user u in ARDB ∩ RCL ; RSRIW, set of recipe
having similarity to recommended recipe Rr on impression words

Step 1: The top 10 words satisfying the condition that their antonyms are within the top 25 words are chosen in eachranking.
Step 2: The words that ranked within the top 10 and their antonyms within the same ranking are chosen.
Step 3: The words ranked within the top 10 are chosen under therestriction that their antonyms are out of ranking.
Step 4: Processing repeats for words ranking below the top 10.

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K. Saito et al. / Journal of Robotics, Networking and Artificial Life 5(3) 204–207

Figure 3 | Sample new user recommendation

Figure 4 | Accuracy recommendations of 10 new users
Figure 2 | Recipe recommendation considering yesterday’s dinner.
ARDB, set of all recipe in database; RY, recipe of yesterday’s dinner;
RSY, recipe similar to yesterday’s dinner; RAW, set of recipe for user u
to want to eat; RHE, the recipe with highest estimation by all user in UL
among RAW; UL, set of user in reference user list

The proposed system recommends a recipe to 10 new participatingusers who were not among the 12 users who entered evaluations intothe database. K was set to 15, and experiments were conductedunder the following two conditions:
Recommendation accuracy = Number of recipes to which the user
replied, “I want to try”/Number of recommended recipes.
Final recommendation accuracy = Number of users that recommended the recipe that the user most wants to try/Number ofusers.
Our experiment was performed on a Dell Inspiron 7559 personal
computer (PC) equipped with Intel Core i7-6700HQ 2.60 GHz
central processing unit and 8.0 GB of random access memory. The
Microsoft Windows 10 Professional operating system (OS) wasinstalled on the PC and Microsoft Visual C++ 6.0 and Microsoft
Visual C++ 2010 were used as the development languages.

6.2. Results and Discussions
Figure 3 shows a sample recommendation for a new user, while
Figure 4 shows the recommendation accuracy of 10 new users. The
mean value of the proposed system’s recommendation accuracy
was 33.0%, while that of the random recommendation was 21.4%.
The value of the final recommendation accuracy of the proposedsystem was 50.0%, while that of the random recommendation wasestimated as lower than 21.4%.
The mean value (33.0%) of the proposed system’s recommendation
accuracy was lower than that (71.1%) by our previously reported
system [2]. From this result, it is thought that a user’s favorite recipes focused in our previously reported system [2] were not necessarily those that he or she wants to try, so it will be necessary tochange the subjective recipe scoring system used in the this study
to consider recipes the user wants to experience in the future. Since
the accuracy value of our proposed system’s final recommendationexceeds the value of random selection, it can be said that our proposed system has proven to be effective.
We then conducted a questionnaire survey of 10 user participants
regarding the recommendations produced by our system. The four
categories below were evaluated:(1) Speaking ease of user to UI.
(2) Time needed to produce recommendation.
(3) Naturalness of UI output as utterance.
(4) Dish display necessity.
For the category (1) results, one participant reported that the UI
was “very easy to talk to”, three participants said it was “somewhat easy to talk to”, five said that “talking was normal”, and one
said it was “slightly hard to talk to”. It should be noted here thatno participant said the UI was “Hard to talk to”. For the category(2) results, no participants reported that time required to output arecommendation was “too short”, “somewhat short”, or “too long”.
Instead, seven participants said the time was “as expected,” and threeK. Saito et al. / Journal of Robotics, Networking and Artificial Life 5(3) 204–207

participants said it was “slightly excessive”. For the category (3)results, one participant said the UI’s utterances were “very natural”,
five said they were “somewhat natural”, one called them “appropriate”, three called them “slightly unnatural”. It should be noted herethat no participant said the UI utterances were “too unnatural”. For
the category (4) result, seven participants said visual displays were“necessary”, two participants said they were “somewhat necessary”,
and one was “undecided”. No participant said the displays were“slightly unnecessary” or “unnecessary”.

7. CONCLUSION
In this paper, we proposed a recipe recommendation system thatcombines collaborative filtering and impression words and is basedon our previously reported system [2]. We showed that our proposedsystem is more effective than random recommendations and that it
has the ability to narrow down potential recipes to a single choice.

207

REFERENCES[1] MMDAgent, available from: http://www.mmdagent.jp/ (accessed25 July 2016).
[2] K. Saito, T. Asada, Y. Yoshitomi, R. Kato, M. Tabuse, A recipe recommendation system using knowledge information and agent,
Proceedings of the Human Interface Symposium, Tokyo, Japan,
2016, pp. 51–54 (in Japanese).
[3] S. Yoshizaki, Y. Yoshitomi, C. Koro, T. Asada, Music recommendation hybrid system for improving recognition ability using
collaborative filtering and impression words. J. Artif. Life Robot.
18 (2013), 109–116.
[4] Sizzle Word Report 2014, available from: http://www.bmft.jp/pdf/services/kotoba14.pdf (accessed 25 July 2016).
[5] Rakuten Recipe, available from: http://recipe.rakuten.co.jp/category/.
[6] Weblio Dictionary, available from: http://thesaurus.weblio.jp/(accessed 25 July 2016).

Authors Introduction
Mr. Keita Saito

Dr. Taro AsadaHe received his B.S., M.S., and Ph.D.
degrees from Kyoto Prefectural Univer­sity in 2002, 2004 and 2010, respectively.
He works as an Associate Professor at
the Graduate School of Life and Environ­mental Sciences of Kyoto Prefectural
University. His current research interests
are human interface and image processing.
HIS, IIEEJ member.

He received his B.S. and M.S. degrees
from Kyoto Prefectural University in
2016, 2018, respectively. He works at
the Food IT System Division of JFE
Systems, Inc.

Dr. Yasunari YoshitomiHe received his B.E., M.E. and Ph.D.
degrees from Kyoto University in 1980,1982 and 1991, respectively. He works asa Professor at the Graduate School of Lifeand Environmental Sciences of Kyoto
Prefectural University. His specialties areapp­lied mathematics and physics, infor­ma­tics environment, intelligent infor­matics.
IEEE, HIS, ORSJ, IPSJ, IEICE, SSJ, JMTA and IIEEJ member.
Dr. Masayoshi Tabuse
He received his M.S. and Ph.D. degrees
from Kobe University in 1985 and1988 respectively. From June 1992 toMarch 2003, he had worked in Miyazaki
University. Since April 2003, he has been
in Kyoto Prefectural University. He works
as a Professor at the Graduate Schoolof Life and Environmental Sciences ofKyoto Prefectural University. His current
research interests are machine learning, computer vision andnatural language processing. IPSJ, IEICE and RSJ member.

Dr. Ryota Kato
He received his B.S., M.S., and Ph.D.
degrees from Kyoto Prefectural University
in 2009, 2011 and 2017, respectively.
He works as a Researcher at the Graduate
School of Life and Environmental
Sciences of Kyoto Prefectural University.
His specialty is information communi­
cation system. HIS member.