Buy Eye-mask Instead of Alarm Clock!:
Graph-based Approach to Identify Functionally
Equal Alternative Products
Tsukasa Hirano1 , Yoshiyuki Shoji1,2[0000−0002−7405−9270] ,Takehiro Yamamoto3[0000−0003−0601−3139] , andMartin J. Dürst1[0000−0001−7568−0766]
1

Aoyama Gakuin University,
Sagamihara, Kanagawa 252–5258, Japanhirano@sw.it.aoyama.ac.jp, duerst@it.aoyama.ac.jp
2
Shizuoka University,
Hamamatsu, Shizuoka 432–8011, Japanshojiy@inf.shizuoka.ac.jp
3
University of Hyogo,
Kobe, Hyogo 651–2197, Japant.yamamoto@sis.u-hyogo.ac.jp

Abstract. This paper proposes a method to analyze product reviews
to identify other products that can achieve the product’s intended use.
When people want to achieve the purpose of “getting up early in themorning,” they generally tend to look only for an “alarm clock.” In contrast, there are several products such as “smart curtains” that can directly achieve the purpose and “sleeping pills” that can indirectly achieve
the purpose by replacing “getting up early in the morning” with “goingto sleep early at night.” The proposed method constructs a bipartite
graph comprising product and uses purpose information from productreview data. Then, the random walk with restart technique is employed
to rank other products that can satisfy the use purpose of the input product. The proposed method was evaluated in subjective experiments, andthe results suggest that the method is both accurate and useful in termsof identifying alternative products that satisfy similar use purposes.
Keywords: Information Retrieval · Review · Product Recommendation· Alternative Achievement

1

Introduction

Tunnel vision, which is also referred to as narrow-mindedness, is a significant
obstacle in terms of product selection while shopping. Tunnel vision is a perceptual phenomenon whereby an individual experiences a significant reductionin peripheral vision. As a result, the individual focuses predominantly on theircentral vision, similar to looking through a tunnel. Most people have bought
something they did not need on impulse due to tunnel vision at least once in2

T. Hirano et al.

their life. For example, after oversleeping and becoming upset, it is common forpeople to impulsively purchase an alarm clock in order to wake up at the necessary time. Conventional recommendation algorithms recommend various alarm
clock devices when the user starts searching for an “alarm clock.” In this case,the user can compare many “alarm clocks” to find the most suitable device forthe given purpose, i.e., waking up at the correct time.
However, solutions to avoid oversleeping are not limited to alarm clocks. For
example, if you take sleeping pills the night before and go to bed early, you may
reduce the risk of missing sleep the next day, and if you buy a pillow that allows
you to sleep soundly, you may wake up more refreshed the next morning, evenafter a short sleep. As a result, when a person focuses singularly on alarm clock
products, they are much more likely to purchase the product without considering
any information about alternative products that are functionally equivalent.” Inmany situations, consumers get tunnel vision and focus their attention on only asingle purpose, thereby making it impossible to find an appropriate alternative
product.
With the increasing ubiquity of the Internet and smartphones, various online
shopping services and customer-to-customer marketplaces have become increasingly common in recent years. As a result, a wide variety of consumers canpurchase desired products easily.
According to a survey conducted by the Japanese government, 73.4% ofJapanese consumers have purchased items on an online shopping site. By age,
69.5% of those in their twenties and 78.0% of those aged 60 and older (regardlessof gender or specific age) are using e-commerce sites 4 .
A problem that can occur when casually shopping on the web in this manner
is impulsive purchasing caused by tunnel vision. Online shopping services havemade it considerably easier to purchase various products; however, it has also
become increasingly difficult for users unfamiliar with the Internet to select themost suitable products. In fact, Zhang et al. [13] demonstrated that visual appeal
and traditional recommendation methods create impulsive purchase intent inonline shopping situations. To prevent this, it is necessary to present informationthat encourages logical thinking in consumers searching for a product to makethem think about the purpose they need to address and the product they needonce they have found it.
Generally, conventional product recommendation systems [5] have used collaborative filtering, which first recommends products of the same type, otherproducts purchased by users who purchased that product, and products used in
combination. Thus, if the user selects “alarm clock,” another type of alarm clock
or a related battery for such devices will be presented as a recommendation. Here,we consider the case where the user searches for an alarm clock to facilitate waking up early in the morning consistently and reliably. Many products can achieve
4

The Ministry of Internal Affairs and Communications Japan: Usage of Internet
shopping and auction flea markets (in Japanese)
https://www.soumu.go.jp/johotsusintokei/whitepaper/ja/r03/pdf/n1100000.
pdf

Buy Eye-mask Instead of Alarm Clock!

3

this purpose, such as optical clocks, smart curtains, and smartwatches; however,conventional recommendation systems will limit the displayed recommendations
to various types of alarm clocks. In other words, the user cannot find a way toachieve the given purpose other than purchasing an alarm clock. Thus, the user
is likely to select an alarm clock even when other suitable options are available.
In terms of recommendation systems, this problem can be solved by identifying
the purpose of the purchase as a query, e.g., “getting up early in the morning.”
Thus, in this paper, we propose a product recommendation algorithm that
outputs a ranking of different functionally equivalent products by defining the
purpose in a query. For example, if the target product is an alarm clock, theproposed algorithm will show candidate purposes, e.g., “wake up early in themorning,” “avoid being late,” and “make a loud sound.” The user can select asingle purpose, e.g., “waking up early in the morning,” which is then taken as
the input to the proposed algorithm. Here, the proposed algorithm determines
if each product in the dataset is suitable for the identified purpose. The algorithm then identifies, ranks, and displays specific alternative products that canachieve the given purpose, e.g., “morning light,” “pillows,” and “supplements.”
To achieve this, we focused on product reviews to realize a product recommendation system that takes the purchase purpose as the query input. Note thatmost online shopping services allow users to post reviews of products, which
may include information about how the purchaser used the product.
We consider using the purpose contained in product review information to
identify products with the same purpose and present them as alternatives. Specifically, assume that an algorithm recommends products that directly include thewords “awake early in the morning” in their reviews. Such an algorithm would
not present alternative products, which is the goal of the proposed algorithm.
The first problem is that review information frequently contains spelling errors
and grouping the same purposes is impossible. The second problem is that a direct search may output common or similar products as effective alternatives. For
example, if the query is “alarm clock,” and the purpose is to “avoid being late,”
it is obvious that many other types of alarm clocks will be output, e.g., “lightclocks” or “wrist watches with alarms.” However, these alternative products do
not broaden the user’s perspective or change the thinking that an alarm clock isa singularly best product to avoid being late. Thus, we created a bipartite graphwith the product, segmented the purpose’s word groups as nodes, and performeda random walk with restart (RWR) calculation. Using bipartite graphs and RWR
allows us to discover a wide range of alternative products, which cannot be realized using conventional search systems. In the context of the “alarm clock”
example, by performing an edge transition for the query “prevent being late,”
an indirect purpose, e.g., “improve the quality of sleep” may be discovered, andvarious products will be output, e.g., “pillow” and “eye-mask.”
To demonstrate the effectiveness of the proposed method, we conducted evaluation experiments using accurate data. The proposed method outputs a rankingof alternative products for as input for the purpose. In the experiments, participants were asked to label how the output products could achieve their purposes,

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T. Hirano et al.

how useful they were for their purchase decisions, and the overall accuracy ofthe product rankings. The usefulness of the proposed method was revealed byevaluating each of the rankings and products.
The remainder of this paper is organized as follows. In Section 2, we summarize related studies and present the position of this study. Section 3 describes
the proposed method, and Section 4 describes an experiment conducted to evaluate and compare the proposed method to a baseline method. The experimental
results are then discussed in Section 5. Finally, the paper is concluded in Section
6.

2

Related Work

We propose a product recommendation algorithm to identify and display alternative products for a given purpose. Thus, this study is closely related to thestudy of product recommendation algorithms and studies related to achievement.

2.1

Recommendation and Search of Products by Purpose

In this study, we attempt to identify products based on the purpose of the product, which is related to the study of product recommendation systems. In aprevious study that considered the relationships among products, Ruining etal. [2] proposed Monomer, a method that treats product relationships as multiple concepts and allows recommendations that are unrestricted by categories orsymmetry. This study differs because the proposed method treats the relationship between products as a single concept that utilizes the relationship between
product reviews.
We attempted to identify and collect the purchase purpose from product review information. In a study that used product reviews, Lei et al. [14] proposeda product recommendation algorithm that divides reviews into two types, i.e.,
one for the user’s actions, and one for the product, and uses a neural networkthat shares a layer trained on each review. In addition, the recommendation
system proposed by Sopheaktra et al. [12] takes product features as input andextracts and uses purposes related to the features from reviews using LDA andWord2Vec. This study differs because the proposed method does not utilize product characteristics. Instead, the proposed method seeks products based on therelationship between the purposes and products. Previous studies have also investigated product recommendation algorithms that focus on the purpose of use.
For example, McAuley et al. [6] proposed a recommendation system that divides
the products they recommend for purchase into interchangeable substitutes andcomplementary products purchased in addition to the identified interchangeable
replacements. The current study differs in that the product is a different type of
product that can realize the given purpose.

Buy Eye-mask Instead of Alarm Clock!

2.2

5

Serendipity of Recommendation

The proposed method differs from conventional recommendation systems in thatit attempts to recommend products that can have a different perspective. Thus,
this study is related to the study of recommending unexpected products [3]. For
example, Kotkov et al. defined serendipitous product recommendations as thosewith three specific elements, i.e., relevance, novelty, and unexpectedness.
As an example of serendipitous product recommendation, Kensuke et al. [7]proposed a recommendation method that uses a bipartite graph of users anditems, as well as a bridging score that represents the degree of association
between the nodes and the degree of the anomaly of the nodes. In addition,
Akiyama et al. [1] constructed a human preference model of serendipity using
data from a questionnaire about serendipity, and they proposed a recommendation method ranked by the length of item distance.
2.3

Achievement Products for the Purpose

The proposed algorithm outputs an alternative product by finding possible alternative purposes for the product. To extract potential alternative purposes,
Pothirattanachaikul et al. [8] proposed a method to extract alternatives that
can achieve the purposes from community question answering (CQA) sites. For
example, assume that we are focusing on the behavior of “taking sleeping pills”
to achieve the purpose of “solving sleep problems.” In that case, we present analternative behavior of “taking a walk before bedtime.” The relationship between
actions and purposes is a bipartite graph that utilizes the question-and-answer
information from the CQA site. In addition, the similarity is ranked to discoveralternative measures.
In addition, Yamamoto et al. [10] and Yang et al. [11] proposed a method
to identify alternatives based on the relationship between the primary purposesand sub-purposes. Here, they defined “exercise” and “diet pills” as sub-goals
to achieve the primary goal of “losing weight,” and they proposed a method
to cluster the sub-goals using sponsored search data. Yang et al. defined subtasks as “selecting a hotel” and “recruiting volunteers,” etc., which are required
to accomplish the main task of “organizing a meeting,” and they proposed amethod that connects queries with the functions described in wikiHow. Based on
the definitions of these alternatives, the proposed method performs calculations
to identify alternative achievement proposals.

3

Graph-based Method to Identify Alternative ProductsThis section describes a method that inputs the purpose for why a user wants
to buy a certain product, and then outputs a ranking of alternative productsthat can satisfy the purpose. To realize this algorithm, the proposed method
extracts sentences from product review data corresponding to the use purpose.
We hypothesize that products with the same purpose are similar and substitutable. Based on this hypothesis, we assume that a graph-based computation

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T. Hirano et al.

is appropriate, and we identify potential alternative products by constructing agraph that represents the relationship between products and purposes.
3.1

Extracting Purposes from Product Review Data

In the proposed method, product review information is used as data to obtain the
purpose of using a given product. The proposed method extracts phrases fromstrings, primarily using syntactic patterns. Note that the dataset considered inthis study is in Japanese; thus, we explain the extraction method using Japanesegrammar and vocabulary. This process differs depending on the target language;
however, for grammatical reasons, it is generally easier in English.
In the review text, only a few parts of the text are related to the purpose; thus,
the proposed method utilizes syntactic patterns to isolate the purposes from thereview text data. Here, a two-step process is employed to identify the purpose.
The first step involves extracting the purpose based on language patterns, andthe second step involves selecting sentences using morphological analysis.
Prior to extracting the purpose, the review data are cleaned in a preprocessing step. For example, sentences that mention shop, shipping, and price areremoved. We also remove sentences that contain terms that are irrelevant to the
purpose, e.g., “postage” and “wrapping.”
An overview of the proposed purpose extraction method is illustrated in
Figure 1. First, the purposes are extracted using regular expressions. Here, if
the extraction utilizes typical syntactic patterns, the sentences may become redundant. Redundant sentences are those that contain many words besides the
purpose word, which can result in an excessive number of nodes when usinggraph processing–based algorithms.
Thus, we must extract on the required words and construct subgraphs thatare effective for the corresponding calculation. In Japanese, due to the grammatical order of words, it is necessary to specify the words before and after tobe interposed when extracting the purpose phrase in a language pattern. The
front patterns include “when” (“-toki ” in Japanese), and the back patterns include “for this purpose” (“-tame-ni ” in Japanese). For example, consider thesentence “... neru toki, kuraku suru tame-ni ...” (“When I sleep, I want to makethe room dark.” For this purpose, I bought an eye-mask.”). Here, the phrase“kuraku suru” (“make the room dark”) is extracted. We manually collected 15
eligible terms for the front and back patterns, and we used their combination.
Sentences containing the purchase purpose are frequently followed by words like“utilize” (“ri-you” in Japanese); thus, the extraction process is performed whenthese words are found after the matching sentence. In addition, sentences exceeding a certain length are excluded because redundant sentences may be extracted
even when a language pattern matching process is employed.
Next, morphological analysis is performed to filter out only those sentences
extracted by syntactic patterns that contain the purpose. Note that some of these
syntactic patterns are commonly used expressions. For example, if a phrase is
extracted with the language pattern “For,” it may express a purpose, as in “Forsleeping well,” or it may refer to an object, as in “For children.” In such cases, itBuy Eye-mask Instead of Alarm Clock!

Item

Review

Item
Extraction by language patterns

I bought it to awake
in the morning.

I bought it to sleep
peacefully at night.

I bought it to sleep
peacefully at night.

Dumbbellmorning, awake

I bought it to awake
in the morning.

Digital alarm
clock

Sleeping pill

Review

awake inthe morning

Purpose extraction,
Graphing

Analogalarm clock

7

sleep
peacefully
at night

night, sleep

muscle, strengthen
It worked well.

I bought it tostrengthen arm
muscles.

Original review data

Strengthenarm muscles

Graph of product and purpose

Fig. 2. Example of nodes created by splitFig. 1. Overview of generating productting purpose into words.
purpose graphs using language pattern
extraction.

is necessary to classify the extracted sentences by focusing on the part of speechof the words. In other words, in this case, if it were a verbal noun phrase, itwould be a purpose, and if it were a noun, it would not be a purpose.
For each syntactic pattern, we created a list of the parts of speech from which
the corresponding purpose could be extracted. Specific to the Japanese language,
any noun can be converted into a verb using the suffix “-suru,” and such verbs
were treated as purposes by extracting the noun part.
Some other verbs related to “be” verbs and presence were listed and removed, e.g., “is,” “are,” “do,” and “exist” (in Japanese: “aru,” “iru,” “suru,”
and “yaru”).
In this study, we used the MeCab morphological analysis library5 . In addition,
as the word dictionary, Mecab-ipadic-NEologd6 , which includes new words andproper technical nouns, was used for the analysis.
3.2

Creating Nodes Comprising Multiple Purpose Terms

The proposed method constructs a bipartite graph using the extracted purposes;
thus, the extracted purpose phrases are converted into a set of several terms to
be used as nodes.
Note that these data can contain many different representations of the same
purpose. For example, the simple purpose of “getting up early in the morning”
5
6

MeCab: https://taku910.github.io/mecab/
Mecab-ipadic-Neologd : https://github.com/neologd/mecab-ipadic-neologd

8

T. Hirano et al.

can be expressed as “waking up before sunrise” or “early rising.” However, thesize of the graph would increase significantly if each of these unique expressions
are used as nodes. Thus, we extract and lemmatize keywords from the purpose
phrases. In the previous example, the words “wake,” “early,” and “morning”
should be extracted. The extracted set of terms is then used as a node.
When extracting important keywords, short sentences are first divided intowords (note that Japanese is not separated by white space; thus, it is necessary
to perform morphological analysis). Then, some words are removed according
to the part of speech, i.e., only noun, adjective, and verb terms are kept, andall other terms are removed. Then, as part of the lemmatization process, allverbs are set to the standard form, and nouns are set to the singular form.
In addition, other keyword candidates are removed based on Japanese uniquefrequent terms. Here, very common words and uncommon words are removed inthis step because they do not contribute to score propagation (even if treated as
nodes in the graph). Finally, the graph is constructed, as shown in Figure 2.
3.3

Creating Bipartite Graph of Product and Purpose

Our algorithm assembles a bipartite graph using the created nodes. Figure 3
shows an overview of the proposed method. The algorithm is based on the hypothesis that if two products share many of the same achievable purposes, they
may be substitutable for each other. Thus, the proposed method attempts to
identify products that can achieve the same purpose by representing and calculating the relationship between similar products and purposes as a bipartite
graph. Here, in the constructed graph, the reviewed products and word groups
included in the purpose are nodes, which are connected by edges. The graphs
are created as follows.
– Select a single product purpose and search for a product that has an edge
to a word node contained in the purpose.
– For all obtained products, we search for products that have an edge to aword node in each purpose.
– The graph is constructed with the products obtained using the above process
and the purpose word groups used in the search as nodes.
Rather than creating a graph using all product and purpose nodes, we selecta particular product’s purpose and create a subgraph using only the productand purpose nodes obtained from the two searches. This process is performed
first because if all nodes are used, the size of the graph matrix will become toolarge to compute. Second, we attempt to prevent the edges from becoming lessrelevant as they are followed. If all nodes are used, there is a risk that they will
be calculated as being related to a particular product, even if they are dozens ofedges away. This is different from the substitutability defined in this study.
In addition, if a node is explored as it is, a recommendation of a purpose
related to another purpose of the first product is made. For example, assume thata pillow has the purpose of “improving the quality of sleep at night” and “curingBuy Eye-mask Instead of Alarm Clock!

Selected
product

1 hop

morning,
awake

★★★★★

★★★★★

I was late a lot, so I
bought it to awake
in the morning.

I got into thehabit of awaking
in the morning.

𝒆(𝒊𝟏 , 𝒑𝟏 )night,
sleep
４hop

3

𝒘(𝒆(𝒊𝟏 , 𝒑𝟏 )) = 𝟏 ∗ 𝟑 = 𝟑𝒊𝟏 𝒆(𝒊𝟏 , 𝒑𝟐 )Extracted within
4 hops

３hop

★★★★★

It was good volume
for me to awake inthe morning.

The weight of the edge bewteen 𝒊𝒊 and 𝒑𝟏 is …
The number of reviews containing“awaking” and “morning”

morning,
awake

2 hop

9

morning, awake

1

𝒑𝟏

4

night,
sleep

𝒊𝟐night, sleep

2

𝒑𝟐

4

muscle,strengthen

Graph of product and purpose

morning, early𝒑𝟑

𝒊𝟑

Subgraph consisting of nodes
within 4 hops

term
𝒊𝒏

𝒆(𝒊𝒏 , 𝒑𝒎 )𝒑𝒎Fig. 3. Subgraph creation with graph Fig. 4. Edge weighting using review texts
containing purposes.
search and extraction.

stiff shoulders,” and that a search that selects the former finds the product. In
this case, the following search finds the pillow again, thereby resulting in a searchconcerning the purpose of “curing stiff shoulders. ” For this purpose, the edge
to the first product is removed for the second and subsequent searches.
Here, let A be the adjacency matrix, let I be the set of commodity nodes,
and let P be the set of destination nodes. In this case, matrix A comprises(|I| + |P |) × (|I| + |P |) dimensions, where the upper right and lower left of thematrix indicate the relationship from the product to the purpose and from the
purpose to the product, respectively. Let Lp (in ) denote the set of P linked to in ,and let Li (pm ) denote the set of I linked to pn . In the upper right of the matrix,
if In is contained in Li (pm ), the value of Anm is 1; otherwise, the value is Anm
is 0.
3.4

Edge Weighting

The weight of each edge is set prior to performing the graph-based computation.
Figure 4 shows an overview of the weighting technique used in the proposed
method. Here, weighting is applied to focus on important words related to the
product. Thus, weighting is performed using the occurrence frequency of words inthe review text data. Some reviews were not extracted as purposes but contained
the exact words as the extracted purposes. Thus, using the occurrence frequencyof the purpose’s words, it is possible to determine which words are essential. Here,
if |Fnm | is the frequency by which word pm appears in the reviews of productin , the weight w(e(in , pm )) of the edge e(in , pm ) is expressed as follows:w(e(in , pm )) = |Fnm |.

(1)

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3.5

T. Hirano et al.

Random Walk with Restart

The method outputs a ranking of products that can achieve the desired purposeby calculating the degree of relevance between the nodes in the graph. Althoughmany algorithms have been proposed to calculate the degree of association between nodes, the proposed method employs the RWR technique. Here, RWR isused in the proposed method based on the hypothesis that products have similar
purposes and that the purposes of a given product have some relationship witheach other. For example, “dish detergent” is unsuitable for removing stains frombaths and cars; however, it can be used on glass. In other words, we can inferthat another detergent used on glass can be used on dishes. These relationships
can be obtained as values using the RWR calculation.
In the following, we describe the relevance calculation method using the RWR
technique. First, the adjacency matrix A, which represents the graph of products
and target word groups created up to the 3.4 clause, is transformed into a transition probability matrix. This transformation is performed by normalization,
where each column is divided by the sum of the weights of the edges departing the node. Here, if the transition probability matrix is A′ , the normalization
equation is defined as follows:
Aij
A′ij = P|I|+|P |
k=1

4

.

(2)

Akj

Evaluation

A subject experiment using real product data acquired from an e-commercesite (Rakuten Ichiba) was conducted to evaluate the usefulness of the proposed
method. Two separate evaluations were considered in this experiment, one forthe top-ranked alternatives and one for the rankings.
4.1

DatasetIn this experiment, we used product review data taken from Rakuten Ichiba,
which is one of Japan’s most popular e-commerce sites. The review data include review sentences, product names, and category data, and such data canbe used to construct appropriate graphs. However, some of the review data were
unsuitable for our purpose; thus, the review data were filtered to eliminate inappropriate data.
First, we excluded categorical data. There are 39 categories in the dataset,and each product is divided into large groups, e.g., “clothing,” “home appliances,” and ”sports.” The review data for products in the categories specified
here were excluded. Examples of the excluded categories are only alternatives,e.g., “clothing” and “food,” and those that cannot be substituted, e.g., “realestate” and “tickets.”
Next, we excluded cases where the same review text was attached to multiple
products. Products with the same review were excluded if they had the same

Buy Eye-mask Instead of Alarm Clock!

11

review text at least once because this would affect the quality of the graph computation. After removing review data according to these processes, we obtained
a dataset with a total of 44,834,987 reviews, and the number of products withreview data was 180,910.
4.2

Implementation

The purposes were extracted from the acquired review data using regular expressions and MeCab, as described in Section 3.1. Finally, we obtained a totalof 339,515 purposes.
Next, the words were segmented using MeCab to create word groups for thenodes. Here, only nouns, verbs, and adjectives were used, and the verbs werestandardized. Finally, a total of 132,015 purpose nodes were obtained.
Then, the graph for the calculations was created. The preprepared query
was divided into words, and the purpose node matching each word group was
acquired. Then, we acquired the products related to the obtained word groups
and searched for the products again using the word groups related to the products. A matrix representing the graphical relationships was created using the
products and purposes obtained from these processes.
Finally, The RWR method was used to calculate the relevance of the productto the purpose. Here, the probability of a random jump to the query node was
set to 0.8, and recursive calculations were performed until the results converged.
4.3

Compared Method

We prepared several variants to evaluate the usefulness of the proposed method.
1. Proposed method. This method calculates the relevance of the productpurpose graphs using the RWR technique.
2. Unweighted graph. Similar to the proposed method; however, edges are
not weighted in this variant.
3. Product description similarity. This method calculates the similarity
between the query and product descriptions using Doc2Vec.
4. Keyword match. This method finds products that contain all query terms
in its review.
5. Random: This method selects products with reviews randomly.
4.4

Evaluation of Alternative Products

We prepared 25 queries and obtained product rankings in advance using themethods described in the previous section. Here, the participants labeled the top
five items in all output rankings. The evaluation items included “Is it a direct
solution?”, “Is it an indirect solution?”, “Did it help you in your purchasing
decision?”. In this evaluation, a four-point scale was used, with four being themost applicable, and one being the most minor practical.

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T. Hirano et al.

Table 1. Precision of each method and each evaluation factor (∗∗ : p < 0.01, ∗ : p <0.05 to Product Descriptions method)
Methods
Proposed Method
Unweighted Graph
Product Description
Keyword Match
Random

4.5

Direct solution Indirect solution Purchase decision # Found**0.44**0.460.34**0.43**0.02

0.05
0.06
0.04
0.04
0.00

**0.50**0.530.14**0.41**0.03

759
759
24
-

Evaluation of Ranking

Three methods that enable ranking were considered in this study, i.e., the proposed method, unweighted graph calculation, and the product description similarity calculation. These three methods were divided into two cases, i.e., withand without category refinement, and a total of six methods were used to label
the output rankings. The evaluation items included “Have you broadened yourperspective?”, “Is there diversity?”, “Did you get motivated to buy products?”,“Comprehensive evaluation.” Here, a four-point scale is used, with four being
the most applicable, and one being the most minor practical.

4.6

Experimental Results

In this study, the average of the responses of two participants was used as theevaluation value. In this case, Cohen’s kappa values [4] for all items in eachexperiment was greater than 0.2, which indicates the reliability of the results.
Table 1 shows the mean values of the reasonable rates obtained in the evaluation experiments of the alternative products. The precision for each query is
one if the average of the evaluated values is more significant than three on a
five-point scale. The number of found is the average number of discoveries for
all queries, the number of found for the proposed method, and the unweighted
method is the number of nodes used in the calculation. We found that the unweighted graphing method was rated highly for all items. The proposed method
exceeded the agreement of the purpose query in all reasonable rates, except for
the “direct solution” item.
Table 2 shows the average values obtained in the ranking evaluation experiments. As can be seen, the proposed method narrowing was evaluated highlyin terms of “perspective,” and the unweighted graph calculation with narrowing for “diversity” received a high evaluation. The unweighted graph calculation
without refinement was evaluated highly for the “motivated” to buy and “overall” rating items. The proposed method exceeds the baseline results (productdescription’s similarity) in all terms.

Buy Eye-mask Instead of Alarm Clock!

13

Table 2. Average participant rating for each method when targeted to a specificproduct area (1 to 4, ∗∗ : p < 0.01, ∗ : p < 0.05 to Product Descriptions method)
Methods

5

Categories Perspective Diversity Motivating Overall

Proposed method
Narrowed
Unweighted Graph
Narrowed
Product Descriptions Narrowed**3.14**3.12
1.72**3.44**3.502.56**3.10 **3.14**3.24 **3.28
2.40
1.78

Proposed Method
Unweighted Graph
Product Descriptions**3.02**3.04
1.98**3.30**3.082.64**3.30 **3.12**3.60 **3.36
2.48
1.96

All
All
All

Discussion

First, an overall evaluation of the proposed method is discussed in terms ofevaluating the alternative products. For all terms, we found that the proposed
method exceeded the agreement on the baseline and the purpose keyword match.
However, the results indicated that the unweighted graph calculation was themost effective method. Nonetheless, there were some cases where the proposed
method was helpful for certain queries.
One such query was “Stretch out the wrinkles in your clothes.” The higher
precision can be attributed to many words related to the purpose, e.g., “electriciron” and “sewing machine.” For queries where the number of words is small andthe types of products that can be explored are somewhat limited, words related
reviews are more likely to appear.
A query for which the proposed method underperformed compared to theweighted computation was “Develop a sense of balance by training.” One possible
reason for the low precision for this query is that some words, e.g., “house” and“home,” that are unrelated to the original purpose, are assigned greater weight.
Another reason may be that the weight is biased toward products with highreview scores.
In addition, a query for which the results of both the proposed method andthe weighted calculation were nearly the same was “Hang a picture on the wall.”
The reason why the precision results were similar for this query may be due to thesmall number of reviews for the selected products. To address this issue, the ratioof the number of reviews to the number of occurrences should be considered. The
RWR algorithm enables the calculation of another indirect purpose or productthat can achieve the same purpose.
The evaluation results of these experiments demonstrate that the proposed
method can identify more products with higher accuracy than general searchmethods.
However, as a future issue, it will be necessary to reduce the number ofoversight of words that express purpose. In the current search, there are cases
where there is no combination of words in the query, and the search results must
be examined. In addition, the extraction of purposes must be improved. In thisstudy, the extraction process was based on language patterns; however, even if

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T. Hirano et al.

the expressions match, this does not necessarily mean that the identified productsatisfies the given purpose. For example, when a product with a review that says
it gets up early has a low rating, it is unlikely to solve the purpose. An effective
weighting method must also be devised. The weighting technique implemented in
the proposed method provides increased diversity; however, accuracy is reduced.
Thus, more effective weighting methods should be investigated in future work.

6

Conclusion

In this paper, to prevent impulse buying, we have proposed a method to discoveralternative products that can achieve the given purpose of using one product.
For this purpose, the proposed method isolates the purpose of the product reviews according to language patterns and constructs a bipartite graph comprising products and purposes. Then, the weights of the edges are adjusted after the
subgraphs are cut out for calculation. The RWR is then employed to calculate
the graph, thereby making it possible to calculate another indirect purpose or
product that can achieve the same purpose.

Acknowledgments
This work was supported by JSPS KAKENHI Grants Number 21H03775, 21H03774,
and 22H03905. We used “Rakuten Dataset 7 ” provided by Rakuten Group, Inc.
via IDR Dataset Service of National Institute of Informatics [9].

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