Location-based Reminder for Memorizing
What Visitors Learn at a Museum
Yoshiyuki Shoji

Kenro Aihara

Martin J. Dürst

shoji@it.aoyama.ac.jp
Aoyama Gakuin University
Kanagawa, Japankenro.aihara@nii.ac.jp
National Institute of Informatics
Tokyo, Japanduerst@it.aoyama.ac.jp
Aoyama Gakuin University
Kanagawa, Japan

Noriko Kando

Takuya Nakaya

Hiroaki Ohshimakando@nii.ac.jp
National Institute of Informatics
Tokyo, Japan

nakaya@sw.it.aoyama.ac.jp
Aoyama Gakuin University
Kanagawa, Japanohshima@ai.u-hyogo.ac.jp
University of Hyogo
Hyogo, Japan

Takehiro Yamamoto

Yusuke Yamamotot.yamamoto@sis.u-hyogo.ac.jp
University of Hyogo
Hyogo, Japan

yamamoto@inf.shizuoka.ac.jp
Shizuoka University
Shizuoka, Japan

ABSTRACTThis paper proposes a method to promote a strongly memorable museum experience by sending reminder notifications when visitors
approach places related to the exhibits. Memory retention studies
have reported that memory that is linked to personal experience
is not easily forgotten. Our method estimates real-world places
that are related to a museum exhibit by linking characteristic terms
that appear in reviews for these places with exhibit commentaries.
Relevance between the characteristic terms is calculated by theirco-occurrence in documents and web search query suggestions.
This enables us to send reminders about exhibits to users’ mobile
devices when they approach related locations in their daily life.
Through experiments conducted at the National Museum of Ethnology in Japan, participants who received notifications about theexhibits remembered more exhibits one week later.

KEYWORDS
Museum, Memory Retention, Notification, Reminder

1

INTRODUCTION

People easily forget what they have learned in books, classes, andmuseums. The content of this paper will also be forgotten; however,this paper proposes a technique for remembering it.
As the saying “Example is better than precept” goes, people are
more likely to remember information that is connected to theirpersonal experiences, rather than knowledge that is simply seenor heard. Human memory can be roughly divided into two types:
semantic and episodic [8, 9]. Semantic memory consists of facts
and knowledge which, while long term, are difficult to remember.
Episodic memory consists of personal experiences which are notlong term on their own, but, because they are connected to personal
experiences, are easier to remember and recall. Such experiencebased episodes are likely to be recalled over and over again. Thus,an important episode can become a semantic memory and, finally,
personal knowledge.

To make what visitors see on site at a museum or in a museum catalog more memorable, we propose a method that strongly connects
exhibits to their personal experience. Museums are extraordinary
and not connected to our personal lives. As a result, there is little
opportunity to recall exhibits in our daily life. Our method focuseson geographical entities (e.g., landmarks, stores, restaurants) toconnect exhibits with experiences in the real world.
Imagine that a visitor visited a museum to learn about ethnology.
He or she was interested in a morin khuur (a Mongolian fiddle) in
the museum. However, the knowledge of “what is a morin khuur
and what are its characteristics” does not make concrete sense and,after a while, may be forgotten. Therefore, when a visitor nears anapparel store, a notification is sent to his or her smartphone thatsays, “You are now visiting the shop of an apparel brand that isfamous for having a horse as its logo. The horse is actually closely
related to the morin khuur! The morin khuur is shaped like a horse’s
head and there is a founding myth about it.” By receiving such a
notification, the existence of the morin khuur is connected to theindividual’s experience and, therefore, this visitor will be more
likely to remember this experience. In the future, when he or she
passes by this apparel store, they may recall and think about themorin khuur. In this way, the location-based reminder notification
makes the museum visit and exhibit more memorable.
This kind of attempt to fix textbook knowledge into memory bylinking knowledge to places or experiences is already common in
education. One of the most typical examples is elementary school
field trips; students walk around a city with a teacher and are
introduced to objects related to what they are learning in school.
For example, the meaning of foreign terms on a signboard can belinked to language learning and the color of hydrangea flowers
can be linked to pH (power of hydrogen) in science experiments.
These methods are in accordance with the Meaningful Reception
Learning theory [1].
Our method analyzes the user behavior log of a museum guideapplication and estimates the exhibits that the visitor is interested
in from the log. For each of these exhibits, the method extracts
keywords that characterize the exhibit from its description. It also

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Name of Location“You seem to be in front ofa certain osteopathic clinic”

Name of Exhibit“Please think about Chikunza
right here!”Snippet of Exhibit Description“This is because the description refers
to a liturgical play of a dancer
wearing the mask to treat boys”

Snippet of Place Review(Omitted due to character limit)“And the review of this location says,
I had my body taken care of here”

Figure 1: Sample of notification when a user approaches anosteopathic clinic.
collects review texts from review sites for all places in a specified
area. The relevance between the terms in the exhibit’s description
and the reviews is calculated, and a notification is created. Visitors
can receive notification messages on a mobile device (e.g., smart
phone), such as “You are in an apparel store right now. Actually, thisstore is associated with the morin khuur because the morin khuur
is shaped like a horse’s head and a review of the store mentions ahorse’s head.”
Figure 1 shows an actual example of our system in which a
notification about a mask was sent to users who approached anosteopathic clinic. This mask, (called a Chikunza), is worn by adancer of an African tribe who is in charge of supporting boys ina liturgical play. This is because terms such as “dancer and treat”
are associated with the terms “body and care” in the osteopath’s
review.
One of the aims of this research is to propose an idea that evaluates the quality of information access in museums by measuring
how much of the visiting experience is retained in the visitor’s
knowledge. For this purpose, we created an application that actually aids memory retention.
To verify the effectiveness of our method, we conducted an experiment at the Museum of Ethnology in Japan (called “Minpaku”).
We used the operation log of the “Minpaku Guide”: an iPad guideapp for the museum originally developed by us. Participants received notifications and were asked whether they remembered theexhibits they saw at the museum one week later.

2

RELATED WORKThis research is strongly related to studies on memory retention
and museum experience, and uses the technology of geographicalinformation recommendations and related term extraction.
Research on memory in the field of psychology has been actively
conducted for a long time. Bower et al. [2] proposed that people
remember things longer when they associate them with their own
memories. Similarly, Weinberg et al. [11] showed that when people
move their bodies during an experience, it is more likely to be remembered. Traditional mnemonics method of loci [12] also supports
the usefulness of linking a place and a topic. Memory retention has
also been studied in the field of informatics. For example, Qiu etal. [6] discuss how user interaction in information retrieval affects

the memorability of the search results. Our research also aims to
make the results of information access in museums and guide apps
more memorable through physical experience.
There has already been some research on making museum experiences more memorable. Falk et al. [3] states that to make museum
experiences knowledgeable, it is important to connect the visiting experience with personal experience and to incorporate it into
personal contexts. Petrelli et al. [5] are conducting research on
encouraging people to look back on their museum experience by
providing personalized online souvenirs.
The recommendation of places based on the user’s interests andpreferences has also been widely studied. Koren et al. [4] proposed
an audio guide that provides personalized recommendations oflocations based on the user’s interests.
To link the learning content to geographic entities, the method
must calculate the relevance between two keywords. As a conceptbased method, Watabe et al. [10] calculated the relevance between
two terms from the co-occurrence in the concept space. Shirakawa
et al. [7] uses Wikipedia; they proposed a method to express therelationship between two terms using the Wikipedia Thesaurus andcombining it with Web searches. In this study, our method follows
these methods to link the terms in the descriptions of exhibits withthose in a place’s review.

3

METHOD

This section describes our method of linking exhibits and geographical entities. The method consists of three steps:(1) extracting characteristic terms from the text of the exhibit’scommentary and the place’s review,(2) calculating the relevancy between characteristic terms, and(3) sending reminder notifications to a smart device.
First, our method extracts characteristic terms from the exhibits
and place reviews. We used term frequency–inverse document
frequency (TF-IDF) for this extraction. Sentences were divided into
terms by a morphological analyzer (terms in Japanese sentences
are not set off by spaces), and verbs and nouns were extracted. The
frequency with which a term appeared in all the descriptions and in
all the reviews was counted and the terms that characterize certain
exhibits and places were ranked by the TF-IDF score.
The method then calculates the relevance of a combination ofcharacteristic terms of exhibits and characteristic terms of reviewsfor places. We calculated the relevance based on three hypotheses:
co-occurrence, category distance, and query suggestion. The cooccurrence score is based on the hypothesis that related terms are
more likely to appear in the same document. For this purpose, we
calculated the percentage of terms that co-occur in all Wikipedia
articles. The category distance score is based on the hypothesisthat terms that are semantically close to each other are also close
in taxonomy. The method uses category information taken from
Wikipedia. The score of a term contained in the title of a Wikipedia
article was calculated based on the degree of overlap of the category to which these articles belong. The query suggestion score isintended to be slightly different from the other relevance scores; ittakes into account amusingness and popularity of the combination.
To do this, we hypothesized that popular combinations of terms
would appear in the users’ search queries. Therefore, the method

Location-based Reminder for Memorizing
What Visitors Learn at a Museum

sets the score as 1 if the suggested query of the search engine forthe term contained another term; otherwise, the score is set as 0.
During the actual calculation, these three values are weighted
and summed up. For the experiment, the co-occurrence weight was
set to 0.2 and the category distance weight was set to 0.8. However,
if a term was included in the query suggestion, the final score was
unconditionally fixed at 1.0. These weights and conditions were
determined empirically through preliminary experiments. It is rare
for a term to accidentally appear at the top of a query suggestion
and, when it does, the combination is meaningful.
Finally, the method actually links the place to the exhibit byaggregating the degree of relevance between their characteristic
terms. In this experiment, places were targeted for notification ifeven one term with a high degree of relevance was included in thereview. Each exhibit has a ranking of which sentences in the review
it is deeply related to. For the top 10 exhibits that the visitor wasinterested in, the system decides in advance which sentences will
be displayed when the user approaches the location.
Every review is linked to a real place and these places havelocation information (i.e., latitude and longitude). Using the GPS
information of the user’s mobile device, the geofence function of
the smartphone displays a notification when the user comes within
a 100-meter radius of the target location. In the application forthis experiment, we used the local notification function without
accessing a server because we were able to pre-calculate where tosend the notification in advance.
As shown in Fig. 1, the title of the actual notification message includes the name of the place, the name of the exhibit, and an imageof the exhibit. The aim is to assist memorization by using pairedassociation-like learning, such as when someone says “clinic”, you
say “Chikunza”. The reason why this place was recommended ispresented under the title. Single sentences containing the relevantcharacteristic term are extracted from the exhibit description andthe place review text, and each is displayed as a snippet. The notification text is written in a catchy, broken tone. Tapping on the
notification displays the full text of the omitted snippets and ahigher resolution image. The user can select one of two buttons,“Aha” or “Not interested”, and they can also choose to ignore thenotification.

4

EXPERIMENT

We conducted two experiments to evaluate whether reminders
affect visitor memory retention. The first was a detailed comparison to clarify whether notifications help users retain textbook-likeknowledge. Participants did not go to the museum site, but rather
used the app as a catalog in the lab. The other was a field user
experiment that evaluated the degree of memory retention andoperational problems when such a system was actually used at themuseum site. In both of these experiments, participants went out
in the city and received notifications (except participants in thecontrol groups), and one week later were asked and answered how
many of their favorite exhibits they remembered.

4.1

Dataset

For the experiments, we used the exhibits data of the National Museum of Ethnology (Minpaku), Japan. We used Minpaku Guide; an
iPad app originally developed by us for navigating in the museum.

WEPIR 2021, 19 March, 2021, Online

Participants can easily search and browse detailed information on
3,031 exhibits that are sampled from over 10,000 exhibits at themuseum. This app can be used as a guide on site at the museum
or as a catalog at home. The app collects user browsing logs andexplicitly lists their favorite exhibits. These features were used todetermine which exhibits to use for reminders.
As place data, we collected online reviews for locations from
Google Maps: 7,393 places in a city in the western Tokyo area (i.e.,near to the first author’s laboratory) and 18,165 places in a city in the
Kobe area (i.e., near to the museum). Both areas are two kilometerssquare and include the downtown area around a terminal station.
All places in the dataset have one or more text reviews, not juststar ratings.

4.2

Comparison Experiment at Laboratory

To evaluate whether notification is effective for knowledge retention, we conducted a subject experiment. We divided eight participants into three groups:• in City: Four participants walked through the city and received notifications,• in Lab: Two participants stayed in the laboratory and received notifications while indoors, and• no Reminder: Two participants received no notifications
at all.
All participants operated the guide application freely for one hourin the laboratory. After that, the top 10 exhibits with the highest
number of accesses were extracted from the logs. We lent iPhones
to two groups of participants, in City and in Lab. The in City
participants went to the downtown area in front of a station andwalked through the area for 60 minutes. During this stroll, they
were sent notifications when they approached places related totheir favorite exhibits. The in Lab participants spent their freetime indoors. Regardless of the participant’s behavior or current
location, the participants received notifications at random times.
The no Reminder participants used the catalog app and then went
home without doing anything.
One week later, we checked how many of the top 10 items the participants had viewed were remembered. We performed this checkin two stages. The first step was to answer the question without
hints; the participants responded by listing all the exhibits they
remembered. The second was with hints; the participants were first
asked to “recall the location where you received the notification.”
After that, the participants provided additional exhibits that they
could recall by associating them with the location.
The results of the experiment are shown on the left side of Table 1.
The participants in the in Lab group remembered the most exhibits
and there were exhibits that they were able to recall with hints.

4.3

User Case Study Onsite at Museum

As a more realistic problem setting, we conducted an additional
experiment that sent notifications while participants were walking
around the city after an onsite visit to the museum. Five participants visited the National Museum of Ethnology and appreciatedits exhibits for about two hours. They used the Minpaku Guide
and selected 15 exhibits by using the app’s favorite list function.
Ten of their favorite exhibits were targeted for notification. A week
later, while walking around the city for another event, our system

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Table 1: Results of the two memory experiments, including
the average number of exhibits for each condition, and theratio of exhibits remembered and received notifications.
Comparison at Laboratory
in City in Lab no Reminder# notification# remember without hint
# remember with hint
Total # remember% remember/notification

8.25
5.75
0.25
6.00
0.57

7.00
5.00
2.50
7.50
0.50

5.50
5.50
-

Museumin City
9.80
5.80
0.20
6.00
0.61

sent reminders of the exhibits to their iPhones. After an additional
week, we asked what exhibits they remembered. The aim of thisexperiment was to disclose problems with and improvements thatcan be made to the system when it is operated in the field. For thispurpose, we conducted a detailed questionnaire on the system’s
usability.
The results of the experiment are shown on the right side ofTable 1. The ratio of the exhibits that were remembered of the 10
exhibits for which notifications were sent was 0.61 and 0.31 for thefive exhibits without notification. Two weeks had passed since thevisit, so the results cannot be directly compared with the results ofthe previous experiment; however, most participants rememberedhalf of the exhibits. Similarly to the comparison experiment, providing hints proved effective for recall. As to usability, we obtained
opinions such as “it is annoying when multiple exhibits are notified
in the same place” and “when notifications are displayed in rapid
succession, the previous notification became inaccessible.”

5

DISCUSSION

Overall, reminding by notification seemed effective for memory retention. In the comparison experiment, the number of exhibits
remembered by the participants differed greatly depending onwhether they received a notification or not. On the other hand,
we could not confirm the effectiveness of linking the notification toplaces. The participants who received notifications while indoors
remembered the most exhibits. This may be due to the experimental setting; the participants in the in City group were forced towalk around the city, which is more unusual than remaining in
an indoor setting. These participants may have been so busy or
distracted while walking around the city that they did not read thenotifications carefully.
In the case of recall with hints, the participants commented that
inappropriate or unexpected combinations helped their recall. The
degree of relevance between characteristic terms is important, but
the unexpectedness of the relationship between exhibits and place
may also be influential. The participants pointed out cases where
the quality of the review text was low and unrelated to the place.
Prior cleansing is therefore needed.
Regarding the user case study, the frequency of notifications is
an issue that needs to be resolved. In this experiment, the notifications were not grouped by location, but were handled in units ofsentences. As a result, several exhibits were linked to one place andseveral notifications were issued in close proximity. In actual operation, it is necessary to select the target place so that notifications
are sent out at equal intervals throughout the city.

6

CONCLUSION

In this research, we proposed a method to help memory retention bylinking real places to museum exhibits and sending out notification
reminders. The experimental results showed that the reminders
increased the number of exhibits remembered by the participants
one week later. On the other hand, we were not able to prove theeffect of linking it to the experience of going out into the city.
As a future challenge, larger-scale and more sophisticated experiments are needed. We should conduct comparative experiments
at a museum site and in a more natural environment to send notifications. Currently, the participants are forced to walk aroundthe city in the experiment setting. This situation is unnatural andcannot connect the exhibits to their real, daily lives. Personalizationis also an important issue. There was a bias in the areas of interest
among the participants and this affected memory retention. The
calculation of the relationship between local objects and exhibits
also needs to be made more precise.
In closing, please say aloud “I read a paper about sending a
notification of an exhibit at the station!” the next time you go
to the station. This will convert the content of this paper into an
experiential memory for you so that it will not be easily forgotten.

ACKNOWLEDGEMENT
This work was supported in part by JSPS KAKENHI Grant Numbers
JP18H03494, JP18H03243, JP18K18161, JP17H00762, and JP16H01756.
It was also supported by the NII Open Collaborative Research 2020
Program, numbers 20S1001, 20AD03 and 20AD04.
We sincerely thank the Japanese National Museum of Ethnology(Minpaku) for offering the exhibit meta-data.

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