=Paper=
{{Paper
|id=Vol-2068/wii7
|storemode=property
|title=Personal Values-based User Modeling from Browsing History of Reviews
|pdfUrl=https://ceur-ws.org/Vol-2068/wii7.pdf
|volume=Vol-2068
|authors=Yasufumi Takama,Suzuto Shimizu,Hiroshi Ishikawa
|dblpUrl=https://dblp.org/rec/conf/iui/TakamaS018
}}
==Personal Values-based User Modeling from Browsing History of Reviews==
https://ceur-ws.org/Vol-2068/wii7.pdf
Personal Values-based User Modeling from Browsing
History of Reviews
Yasufumi Takama Suzuto Shimizu Hiroshi Ishikawa
Tokyo Metropolitan University Tokyo Metropolitan University Tokyo Metropolitan University
Tokyo, Japan Tokyo, Japan Tokyo, Japan
ytakama@tmu.ac.jp
ABSTRACT The CF is one of common and successful approaches of
This paper proposes a user modeling method from user’s recommendation, and those variations and extensions have
browsing history of reviews. Personal values-based recom- been studied by many researchers. Variations include item-
mendation method has been proposed, which models users’ based[10], matrix factorization-based[8, 9, 16], and graph-
personal values as the effect of item’s attribute on their de- based approaches[2]. Extensions include introduction of ad-
cision making. While existing method obtains a user model ditional information for calculating inter-user similarity[1,
from reviews posted by a user, this paper proposes to obtain it 13, 11]. This paper focuses on one of those extensions: intro-
from reviews a user consulted for decision making. In order duction of personal values[4]. Personal values and personali-
to identify an attribute that affects on user’s decision making ties are supposed to be important factors in decision making,
efficiently, the proposed method dynamically selects reviews and they have recently received attention by those studying
mentioning attributes on which a user might put priority and recommendation[4, 12]. In particular, the Rating Matching
presented to the user. A method for selecting items to recom- Rate (RMRate), which estimates the effect of an item’s at-
mend based on the obtained user models is also proposed. An tributes on a user’s rating[4], has been proposed for model-
experimental result with test participants shows the effective- ing users’ personal values. Its effectiveness for recommenda-
ness of the proposed method. tion has been shown in terms of content-based approach[4],
CF[17, 18], and item modeling[19].
ACM Classification Keywords Existing studies obtain user models based on RMRate (called
H.5.m. Information Interfaces and Presentation (e.g. HCI): PV model hereinafter) from reviews posted by target users,
Miscellaneous which limits its applicable situations. That is, it can be only
applied to online review sites with attribute-level evaluations.
Author Keywords Even though attribute-level evaluations are available, major-
Recommender system; personal values; user modeling; ity of users on online review sites seldom post reviews. The
online reviews. PV model cannot be obtained for such users.
This paper focuses on the latter problem. In order to calculate
INTRODUCTION PV model for users posting no review, this paper proposes a
This paper proposes to obtain user models reflecting their per- method to obtain it from users’ histories of browsing reviews
sonal values by analyzing their record of browsing online re- posted by others. A method for recommending items based
views. The obtained models are used for recommendation. on the obtained PV models is also proposed, and those effec-
tiveness are shown by experiments with test participants.
In recent years, users have made huge numbers of reviews
and ratings online. Such social big data[5] can be utilized for
enriching our lives in various ways, including recommenda- RELATED WORKS
tion. In order to promote products, it is necessary to establish This section briefly introduces studies utilizing personality
a method for predicting users’ preferences and recommend- and personal values for recommender systems. Personal val-
ing suitable items to them. As ratings are supposed to reflect ues and personality determine the characteristics of a user’s
users’ opinions about items, they can be used to estimate their decision making, and they have been used in marketing.
preferences. Collaborative filtering (CF)[14] and its related Jayawardhena modeled a hierarchical relationship among
algorithms are based on this idea. personal values, attitudes, and behaviors in e-shopping[6].
Wu et al. proposed a method for recommending diversified
items in terms of the most important attributes[20]. In their
study, the degree of diversity is determined from the relation-
ship between the user’s personality and his/her needs for di-
versity.
These studies have shown that personal values are one of
©2018. Copyright for the individual papers remains with the authors. the main factors affecting consumption habits. However,
Copying permitted for private and academic purposes. they model users’ personal values and personality with ab-
WII’18, March 11, 2018, Tokyo, Japan
stract factors such as the Rokeach Value Survey[15] and Big Actually, extracting mentioned attributes with sentiment from
Five[7], which have no intuitive relationship with the items to reviews accurately is difficult even with the state-of-the art
be recommended[12]. text mining techniques[21]. Instead of applying text mining
techniques, this paper utilizes attribute-level evaluations at-
As a more direct approach, Hattori et al.[4] have proposed tached to reviews. That is, this paper supposes online review
a personal values-based user modeling using Rate Matching sites which have attribute-level evaluations. As a review ex-
Rate (RMRate). A user’s personal values are modeled as the plains its reviewer’s opinion about a target item, it is assumed
effect each attribute an item has on his/her decisions. Given that a reviewer makes positive comment on an attribute if s/he
data including users’ item-level evaluation (i.e. rating) and
positively evaluates it.
attribute-level evaluation, the RMRate of ui relative to an at-
tribute ak is calculated as This paper considers that reviews to be presented to users for
obtaining their feedback should satisfy the following condi-
tions.
∑x j ∈Ii δ (pi j , pkij )
RMRik = , (1) 1. Polarity of an opinion about an attribute mentioned in a re-
|Ii | view is the same as the polarity of attribute-level evaluation
explicitly given by a user.
where Ii is a set of items rated by ui , pi j is the polarity of item-
level evaluation (positive or negative) of ui on item x j , pkij is 2. A review mentions some attributes as evidence of evalua-
tion.
the porality of attribute-level evaluation of ui on ak of x j . The
function δ (x, y) returns 1 if x is equal to y, 0 otherwise. 3. Polarity of evaluations of all attributes are not be the same.
The personal values-based CF[18] calculates inter-user simi- The first condition is required to guarantee the above-
larity on the basis of PV models. Given a set of attributes of mentioned assumption. The proposed method supposes that
an item (A), a PV model of ui is represented as |A|-th dimen- users make a decision by reading reviews. Therefore, if the
sional vector, which consists of RMRik (ak ∈ A). Pearson cor- second condition is not satisfied, a user reading a review can-
relation between PV models is calculated among users, which not understand the reason why a reviewer made such an eval-
is used to find neighborhood users. uation for attributes. The third condition is considered to
identify attributes focused by a user.
One of advantages of the personal values-based CF is that a
matrix used for calculating inter-user similarity tends to be As it is difficult to automatically collect reviews satisfying
dense compared with user-item matrix, because the number these conditions with high accuracy, we manually examined
of attributes of an item is usually much smaller than that of collected reviews and constructed a database.
items. Therefore, the number of users to which the similarity
to a target user can be calculated is expected to be large. Modeling with dynamic review presentation
The proposed modeling process is shown in Fig. 1. From
PV MODELING FROM BROWSING HISTORY the constructed database, a set of reviews is selected and pre-
Outline of proposed approach
sented to users to obtain their feedback. In this paper, 3 re-
views are presented to users at the same time. A user feed-
In order to obtain PV model, not only item-level evaluation
back includes the user’s rating to the item (5-point scale, bi-
of a target user on items, but also attribute-level evaluations
nary, etc.) and one review that s/he think is the most helpful
are necessary. Instead of analyzing reviews posted by target
to determine the rating. Based on these feedback, RMRate of
users, as done by existing studies, this paper tries to estimate
attributes are updated. That is, polarity of user’s rating corre-
users’ personal values from their history of browsing reviews.
sponds to pi j in Eq. (1), and that of attribute-level evaluation
Note that this section uses a term ‘user’ as a person for which attached to a review corresponds to pkij .
a user model is obtained; ‘reviewer’ is used as a person who
posted reviews. Let us consider the case that a user is going An important thing to consider in this algorithm is how to
to make a decision on whether or not to buy a certain camera determine reviews which are presented to users. It is incon-
by reference to the following 3 reviews. venient for users if they have to interact with recommender
systems many times before receiving recommended items.
1. The image quality of this camera is good. Therefore, this paper aims to identify at least one attribute on
2. It is easy to operate this camera with a single touch of but- which a user would put priority for his/her decision making
tons. as soon as possible. Even though complete PV model is not
obtained, recommender systems could start recommendation
3. This camera is lightweight and suitable for bringing it any- based on a single attribute on which a user put priority.
where.
For the first loop, reviews are randomly selected from the
If this user decides to buy this camera following the first re- database so that every attribute can be mentioned in at least
view, s/he is supposed to put priority on image quality when one of those reviews. In the subsequent loops, reviews are
s/he evaluates cameras. Therefore, RMRate can be calculated selected so as to satisfy the following conditions. Here, target
by identifying attributes mentioned as positive / negative in attribute means an attribute of which RMRate at this time is
reviews. the highest among all attributes.
Start
Scorei (x j , ui , c j ) = ∑ {ekj − ekc j } · RMR2ik , (3)
Present reviews ak ∈Ai
{ }
∑al ∈A RMRil
Ai = ak |RMRik ≥ , (4)
User judgment |A|
Select reviews
Update
RMRate where c j is an item category to which x j belongs, ekj is average
evaluation for ak of x j , ekc j is average evaluation for ak of
Repeat? items belonging to c j . As these average evaluations, we used
the values released on the online review site.
End The score is calculated based on only the attributes of which
target user’s RMRate is higher than average of his / her RM-
Figure 1. Procedure of modeling process.
Rate for all attributes (Eq. (4)). We employ it in order to
focus on attributes which strongly affect user’s decision mak-
ing. For the same reason, we use RMRate squared for the
1. Present at least one review that positively evaluates target calculation.
attribute.
EXPERIMENTS
2. Present at least one review that negatively evaluates target
attribute. Settings
An experiment with test participants is conducted. The ex-
3. Reviews should have the highest score calculated as Eq.
periment is divided into two phases: user modeling and rec-
(2) while satisfying conditions 1 and 2.
ommendation phases. We asked 20 graduate / undergraduate
students in engineering field to take part in the experiment.
∑k |ekr − er | · RMR2ik In user modeling phase, proposed dynamic review presenta-
Scorer (r, ui ) = log Nr , (2)
Kr tion method is compared with random presentation method.
In both methods, 3 reviews about different hotels are com-
where ui is a user, r is a review, ekr is evaluation of r to an bined into one set. Test participants were asked to evaluate
attribute k, and er is r’s average evaluation over all attributes. different 20 sets as if they were going to book a hotel for the
The Nr and Kr are the number of characters and mentioned at- specified purpose.
tributes in r, respectively. This equation gives high score for a Reviews and hotel information were collected from online
review when evaluation to the attribute, of which current RM- hotel review site 4travel1 . The number of collected reviews is
Rate is high, is higher / lower than other attributes. The Kr in 592. Regarding polarity of attribute-level evaluation, which
denominator plays a role to give priority on reviews focusing is required for calculating RMRate, average evaluation over
on specific attributes. Equation (2) also considers the length all attributes is calculated for each review. If evaluation of an
(number of characters) of reviews, because we found in the attribute is equal to or more than the average, it is regarded as
preliminary experiment that users tended to consult longer re- positive evaluation, and vice versa. The 4travel employs 7 at-
views than shorter ones. tributes: access, cost performance (CP), service, room, bath,
The RMRate is calculated based on the correspondence of meal, and barrier-free. As it is supposed that whether a hotels
polarity between item-level evaluation (rating) and attribute- is barrier-free or not would not affect decision making of test
level evaluation. Therefore, presenting reviews satisfying participants in this experiment, we removed it.
conditions 1 and 2 aims to obtain a feedback regarding We supposed two purpose of booking hotels, i.e. for business
whether or not polarity of attribute-level evaluation is the and sightseeing, and prepared two datasets for each purpose.
same as that of his / her rating to target item. As a termi- The test participants were divided into 4 groups (5 persons
nation condition, we decide to repeat presenting reviews 20 each) as shown in Table 1. We designed the experiment so
times. that hotels in different area are presented in different presenta-
tion method. As the purpose of booking hotels is supposed to
Recommender system based on PV models
affect participants’ decision making, datasets used for a par-
This subsection describes a recommender system based on
ticipant belong to the same purpose for keeping consistency
PV models obtained as described in the previous subsection.
of his/her evaluation. The order of presentation methods was
A straightforward approach is to recommend items to which
rotated so as to remove the order effect.
predicted rating for a user is higher than others. Instead of
predicting ratings, this paper proposes to estimate a degree of In recommendation phase, 10 hotels are selected based on a
recommendation for an item based on user’s PV model. user model obtained by each presentation method. For the
comparison purpose, additional 10 hotels are also selected
Given a set of RMRate of a user ui ({RMRik |ak ∈ A}), a score
of an item x j is defined as follows. 1 http://4travel.jp/
Group Dynamic Random Presentation method RMRate ≥ 10 <10
SightseeingA Tokyo, Hokkaido Dynamic ≥ 0.7 28 2
Kanagawa presentation ≥ 0.8 17 0
SightseeingB Hokkaido Tokyo, Random ≥ 0.7 13 21
Kanagawa presentation ≥ 0.8 9 12
BusinessA Osaka, Kyoto Tokyo, Aichi, Table 4. Number of selected reviews.
Fukuoka
BusinessB Tokyo, Aichi, Osaka, Kyoto Purpose Dynamic Random Satisfaction
Fukuoka Sightseeing 0.720 0.800 0.670
Table 1. Used dataset for modeling. Business 0.630 0.710 0.580
Total 0.675 0.755 0.625
Table 5. Comparison of precision
Group Dynamic Random
SightseeingA Osaka, Hyogo, Okinawa
Kyoto views than random presentation method. It means that when
SightseeingB Okinawa Osaka, Hyogo, an attribute has high RMRate, dynamic presentation method
Kyoto estimates it based on enough information compared with ran-
BusinessA Kanagawa Hyogo, Kyoto dom presentation method.
BusinessB Hyogo, Kyoto Kanagawa
Table 2. Used dataset for recommendation. Result of Recommendation
Table 5 shows average precision: the ratio of items test partic-
ipants judged as positive to all recommended items. Both of
based on review site’s satisfaction ranking. Therefore, each dynamic and random presentation methods achieved higher
participant was asked to evaluate at most 30 hotels; if differ- precision than satisfaction ranking regardless of purpose of
ent methods select the same hotels, the number of presented booking hotels. This result shows the effectiveness of model-
hotels is less than 30. The order of presenting items was shuf- ing users’ personal values from browsing histories of reviews.
fled so that the participants could not know by which method It is also shown that precision by dynamic presentation
(model) a hotel was selected. We prepared different datasets method is lower than that by random method. This result cor-
from modeling phase as shown in Table 2. In the dataset, responds to the fact that dynamic presentation method puts
we removed hotels which were evaluated as 4 or more for priority on fast estimation rather than exhaustive estimation.
all attributes, as such hotels are preferred by almost every- That is, identifying attributes with high RMRate as many as
one regardless of their personal values. For each of presented possible is expected to be effective in terms of accuracy.
hotels, test participants were asked to evaluate it as either pos-
itive or negative.
CONCLUSION
Result of User modeling
This paper proposed a method for obtaining personal values-
based user models from user’s browsing history of reviews.
After the experiment, test participants were asked to answer
The proposed method dynamically selects and presents re-
attributes which they concerned. Table 3 shows average RM-
views mentioning attributes on which a user might put pri-
Rate over attributes they concerned. The table shows that
ority. A method for selecting items to recommend based on
average RMRate by random presentation method is higher
the obtained user models was also proposed. An experimen-
than that of dynamic presentation method for all groups. It
tal result with test participants shows user models obtained
is because dynamic presentation method focuses on specific
from browsing history achieved higher recommendation ac-
attributes, and estimation for other attributes is not enough
curacy than recommendation based on a review site’s satis-
compared with random presentation method.
faction ranking. It is also shown that proposed dynamic pre-
Table 4 compares the number of reviews selected by test par- sentation method is effective for identifying specific attributes
ticipants. The number of selected reviews is counted for each of high RMRate from relatively many reviews.
attribute of which RMRates is relatively high: 0.7 or more
As the number of read-only users is much larger than those
(≥ 0.7) / 0.8 or more (≥ 0.8). Each cell shows the number of
posting reviews, the proposed method will contribute to ex-
attributes, for which 10 or more (≥ 10) / less than 10 (< 10)
tend the applicability of personal values-based recommender
reviews were respectively selected. The table shows that dy-
systems. Future work includes application to other kinds of
namic presentation method estimates RMRate from much re-
items, as well as automatic collection of reviews to be used
for user modeling.
Group Dynamic Random
SightseeingA 0.536 0.547 ACKNOWLEDGMENTS
SightseeingB 0.567 0.675 This work was partly supported by Grant-in-Aid for Research
BusinessA 0.483 0.636 on Priority Areas, Tokyo Metropolitan University, “Research
BusinessB 0.538 0.744 on social big data" and JSPS KAKENHI Grant Numbers
Table 3. Average RMRate for concerned attributes. JP15H02780 and JP16K12535.
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