    \documentclass[english]{jnlp_1.4}

\usepackage{jnlpbbl_1.3}
\usepackage[dvipdfmx]{graphicx}
\usepackage{array}
\usepackage{hangcaption_jnlp}

    \usepackage{amsmath}


\Volume{24}
\Number{4}
\Month{September}
\Year{2017}

\received{2016}{11}{29}
\revised{2017}{4}{16}
\accepted{2017}{6}{7}

\setcounter{page}{579}

\etitle{Cross-lingual Product Recommendation System Using Collaborative Filtering}
\eauthor{Kanako Komiya\affiref{Ibaraki} \and Minoru Sasaki\affiref{Ibaraki} \and Hiroyuki Shinnou\affiref{Ibaraki} \and Yoshiyuki Kotani\affiref{TUAT}}
\eabstract{
We developed a cross-lingual recommender system using collaborative filtering with English-Japanese translation pairs of product names to help non-Japanese buyers who speak English when they are visiting Japanese shopping websites. Customer purchase histories at an English shopping site and those at another Japanese shopping site were used for the experiments. Two experiments were conducted to evaluate the system. They were (1) two-fold cross validation where half of the translation pairs were masked and (2) experiments where all of the translation pairs were used. The precisions, recalls, and mean reciprocal ranks (MRRs) of the system were evaluated to assess the general performance of the recommender system in the first set of experiments. We investigated the effect formatting the translation pairs and the performance according to the type of feature value of the vectors (binary versus rating values). In contrast, the kind of items that were recommended in a more realistic scenario were shown in the second experiment. The results reveal that masked items were found more efficiently than when the bestseller recommender system was used and, further, that items only on the Japanese site that seemed to be related to the buyers' interests could be found by the system in the more realistic scenario.
}
\ekeywords{Cross-lingual, Recommendation, Collaborative Filtering, Translation Pair}

\headauthor{Komyia et al.}
\headtitle{Cross-lingual Product Recommendation System Using Collaborative Filtering}

\affilabel{Ibaraki}{}{Ibaraki University}
\affilabel{TUAT}{}{Tokyo University of Agriculture and Technology}


\begin{document}

\maketitle


\section{Introduction}

Japanese pop culture such as that exemplified by manga, anime, and gaming has gained popularity with the younger generation in recent years. In addition, e-commerce has become widely used throughout the world and had enabled people to purchase products from abroad. However, there are some cases in which non-Japanese buyers are unable to find the products they want through Japanese shopping websites because the websites require Japanese queries. It is particularly difficult to translate product names such as the titles of anime or movies using machine translation. We developed a cross-lingual recommender system using collaborative filtering with English-Japanese translation pairs of product names to alleviate this problem. Even if non-Japanese buyers cannot formulate Japanese queries on Japanese shopping websites, the recommender system should capture their interests from their purchase histories and recommend various products that they want. 

Since we had no customer purchase histories for shopping sites in both English and Japanese, the customer purchase histories at an English shopping site and those at another Japanese shopping site were used together for the experiments. The precisions, recalls, and mean reciprocal ranks (MRRs) of four types of systems were evaluated to investigate the performance of the cross-lingual recommendation. Two kinds of experiments were conducted to evaluate the system: (1) two-fold cross validation, where half of the translation pairs were masked and (2) experiments where all the translation pairs were used. The experiments reveal that the system with collaborative filtering outperformed a bestseller recommender system and products only at the Japanese site that seemed to be related to buyers' interests could be found by the system. 

This paper is structured as follows. 
Section \ref{Sec:Related Work} reviews related work on recommendations and other trials to solve the problem where 
non-Japanese buyers cannot buy some products via Japanese shopping sites. 
Section \ref{Sec:Recommendation System to Help Non Japanese Buyers} explains the outline of the recommendation system that we developed.
Section \ref{Sec:Data} describes the data we used, Section \ref{Sec:Item Identification and Recommendation} demonstrates two processes to achieve our system, 
and Section \ref{Sec:Experiment} explains the experimental settings. 
We present the results in Section
\ref{Sec:Result} and discuss them in Section \ref{Sec:Disscusion}. Finally, we conclude the paper in Section \ref{Sec:Conclusion}.


\section{Related Work}
\label{Sec:Related Work}

Recommender systems, which involve recommending items such as products, pages, and articles, have been studied intensively in recent years. 
Methods of recommendation can be categorized into two types, i.e., content based and collaborative filtering based recommendations. 
We focused on the latter type in the current study. 

There has been a large amount of work on the similarities between users for collaborative filtering. 
For example, \cite{pn} defined similarities that took into consideration the strength of links between users on social network services (SNSs) and they used a graph based on similarities between links on SNSs and customer purchase histories. 

In addition, \cite{kawamae} proposed collaborative filtering based on the relationships between users who tended to purchase products ahead of other users 
by considering changes in users' interests. 
\cite{wiki} proposed a method of recommendation using bookmark data and content from Wikipedia to consider the serendipity of recommendations.
\cite{Musto} proposed content-based recommendation and \cite{Ozsoy} proposed an item recommendation system using the technique of word embedding. 

Our purpose is to help the non-Japanese users of Japanese shopping sites who speak English to purchase products that are sold only on Japanese shopping sites. One way to solve this problem is to use machine translation techniques. However, it is difficult for machines to translate the titles of the books or movies because they contain few literal translations. In addition, machine translation techniques cannot be used, especially when we have no parallel corpora. 
 \cite{Tsuji} proposed transliteration from Japanese product names to alphabetical queries to address this problem. We developed a cross-lingual recommender system and exploited another way of recommending products that are only sold on a Japanese shopping site to non-Japanese users. We believe that these two methods could be used together. 
 
To our knowledge, the closest work to ours is the cross-lingual paper or keyword recommender systems proposed by \cite{Uchiyama} and \cite{Takasu}. 
However, they recommended papers or keywords for the abstracts of papers rather than products such as books and movies, and they did not use translation pairs but parallel corpora instead. 

Recommendations using Linked Open Data (LOD) also correlate with our system because they also use no parallel corpora. 
\cite{Hau} and \cite{Mirizzi} made cross-lingual recommendations using LOD without using collaborative filtering. However, we extracted the translation pairs from Wikipedia. 

In addition, this research has a close relation to cross-domain recommender systems. According to \cite{Cantador}, our research belongs to linked-domain recommendation.

The contributions of this paper are as follows. 
(1) We achieved a cross-lingual recommendation system without any parallel corpora. We conducted linked-domain recommendation using simple collaborative filtering with only translation pairs and customer purchase histories at two shopping sites in different languages. 
(2) We assessed our system using cross validation of the translation pairs. This contribution enabled the cross-lingual recommender systems to be evaluated. In addition to the evaluation of the system, we showed some examples in a more realistic scenario. 


\section{Recommendation System to Help Non-Japanese Buyers}
\label{Sec:Recommendation System to Help Non Japanese Buyers}

Figure \ref{dfd} depicts the data flow diagram of the recommender system using collaborative filtering with translation pairs.  
Hereafter, buyers from Japanese shopping sites are referred to as ``Japanese users,'' and buyers from English shopping sites are referred to as ``English users.''

\begin{figure}[t]
\begin{center}
\includegraphics{24-4ia3f1.eps}
\end{center}
\caption{Data flow diagram}
\label{dfd}
\end{figure}

As Figure \ref{dfd} shows, Japanese items are recommended for each English user when the customer purchase history of that user is input into the system. 
The system performs two processes, item identification and recommendation, that is, (1) the items at an English shopping site are identified as those at a Japanese shopping site using translation pairs extracted from Wikipedia and (2) the items at the Japanese shopping site are recommended using collaborative filtering.  


\section{Data}
\label{Sec:Data}

Two kinds of data, i.e., the customer purchase histories at English and Japanese shopping sites and English-Japanese translation pairs, were necessary to recommend items at a Japanese shopping site to non-Japanese buyers. 
Because we had no customer purchase histories of a shopping site in both English and Japanese, customer purchase histories from an English shopping site and those from another Japanese shopping site were used together in the experiments. 

The review data of the Rakuten Ichiba in Rakuten Data Release\footnote{http://rit.rakuten.co.jp/opendata.html} and 
MovieLens 10~M Dataset of GroupLens Research \cite{MovieLens}\footnote{http://www.grouplens.org/} were used for the customer purchase histories on Japanese and English shopping sites, respectively. 
GroupLens Research has data on the movies. However, the Rakuten Data Release contains data on products in various genres. 
Therefore, the reviews from Rakuten Books,\footnote{http://books.rakuten.co.jp/} which includes data on movies and books, were extracted for the experiments. We used the reviews as the customer purchase histories because they could only be written by users who had bought the items. We used not only movie data but also book data because the original stories of some movies were first written as books and movies are often novelized. 
The customer purchase histories we used are transactions that consist of three items of data: user IDs, product names, and their ratings. 
The ratings were graded by each user on a scale from one to five. Hereafter, items that were bought at both the English and Japanese sites are referred to as ``common items.'' and are important because our system uses collaborative filtering. 

The numbers of transactions, users, and transactions per user of the original data and the data of common items 
according to language are listed in Table \ref{Tab:The num of transactions and users} (in the column labeled ``Without'').

\begin{table}[b]
\caption{Statistics of the original data and data of the common items}
\label{Tab:The num of transactions and users}
\input{03table01.txt}
\end{table}

The English-Japanese translation pairs were collected from Japanese Wikipedia\footnote{http://ja.wikipedia.org/wiki/ (accessed 2012/12/20)} using regular expressions.\footnote{Wikipedia data only contain translation pairs for famous items. It is desirable to use methods that generate more translation pairs to alleviate the sparsity of the data. For example, image recognition could be used in the future.} 
Because the translation pairs consisted of Japanese movie titles extracted from their original titles, they included not only English translations but also translations into other languages such as French, Spanish, and Chinese. 
Table \ref{Tab:The num of transactions and users}  lists the number of translation pairs, number of item types\footnote{We used the word ``type'' to distinguish the number of types from the number of tokens, i.e., the frequency. 
For example, the number of item types equals that of the product IDs if the products have them.} in the original data, number of item types with translations in the original data, and common items (in the column labeled ``Without'').


\section{Item Identification and Recommendation}
\label{Sec:Item Identification and Recommendation}

As Figure \ref{dfd} illustrates, the recommender system using collaborative filtering with translation pairs consists of two processes: item identification and recommendation using collaborative filtering. We used collaborative filtering with item overlap as Figure \ref{ItemOverlap} shows (see  \cite{Cantador}).

\begin{figure}[b]
\begin{center}
\includegraphics{24-4ia3f2.eps}
\end{center}
\caption{Collaborative filtering with item overlap}
\label{ItemOverlap}
\end{figure}
\begin{figure}[b]
\begin{center}
\includegraphics{24-4ia3f3.eps}
\end{center}
\caption{Examples of Japanese customer purchase histories}
\label{Fig:The examples of Japanese customer buying histories}
\end{figure}

\begin{figure}[t]
\begin{center}
\includegraphics{24-4ia3f4.eps}
\end{center}
\caption{Examples of English customer purchase histories}
\label{Fig:The examples of English customer buying histories}
\end{figure}
\begin{figure}[t]
\begin{center}
\includegraphics{24-4ia3f5.eps}
\end{center}
\caption{Examples of translation pairs}
\label{Fig:The examples of the translation pairs}
\end{figure}


\subsection{Item Identification}
\label{ Item Identification }

The same items at the two sites are identified using English-Japanese translation pairs 
because they have different product names according to the language. 
Figures \ref{Fig:The examples of Japanese customer buying histories}, \ref{Fig:The examples of English customer buying histories} 
and \ref{Fig:The examples of the translation pairs} have examples of Japanese customer purchase histories, English customer purchase histories, and the translation pairs. The ones above the lines are the original text. 
As Figures \ref{Fig:The examples of Japanese customer buying histories} shows, 
Japanese customer purchase histories contain noise like ``【送料無料】'' (freight free), ``（予約）'' (pre-order), and ``(Book 2).'' These expressions should be removed to identify the product names. 
In addition, the formats of product names are varied: {\it The Godfather} in the translation pair list is {\it Godfather, The} in the customer purchase histories of the English shopping site. 
 This kind of difference often prevents the products from being correctly identified. 
 Therefore, we formatted the product names to improve product identification and called this process ``formatting.'' 

The procedures of this process are as follows.
\begin{enumerate}
\item Expressions within brackets are removed to remove expressions such as ``【送料無料】'' (freight free), ``（予約）'' (pre-order), and ``(Book 2).''
\item All the Japanese one-byte characters are converted into two-byte characters.
\item  All the two-byte Latin characters used on Japanese shopping sites are converted into one-byte characters.
\item All the Latin characters are converted into lower case.
\item Articles are removed.
\item Punctuation, marks such as apostrophes, and marks used on Japanese shopping sites such as ``＊'' and ``♪'' are removed.
\item Expressions like ``special edition,'' ``Blu-ray Disc Video,'' and ``DVD コレクターズBOX1'' (DVD collectors BOX1) are removed using a stop words list, which included 65 words.
\end{enumerate}

The text under the lines in Figures \ref{Fig:The examples of Japanese customer buying histories}, \ref{Fig:The examples of English customer buying histories} 
and \ref{Fig:The examples of the translation pairs} are formatted examples.

In addition, Table \ref{Tab:The num of transactions and users} shows that the number of the original data items  decreased\footnote{All the words in some transactions are removed by formatting.} 
but the number of common items increased as a results of the formatting. 
It also shows that the formatting decreased the number of  translation pairs and the type of items in the original data, 
but increased the number of types of items with a translation.

After formatting, the items at the English shopping site are identified as those at the Japanese shopping site using the translation pairs extracted from Wikipedia. 
There are some items that have no translation, but they have no impact on the collaborative filtering. 
In this study, we demonstrate the effect of the item identification formatting.


\subsection{Recommendation}
\label{ Recommendation }

We used collaborative filtering for the recommendations. 
Note that ordinary collaborative filtering can be used now that the items at the English shopping site are identified on the Japanese shopping site. 
The steps of this process are as follows. 

\begin{enumerate}
\item Vectors for English and Japanese users are generated. The features of the user vectors are the indices of the products and their values are binary, i.e., bought or not, or their ratings.
\item Similarities between the English and Japanese users are calculated. Cosine similarities are used. 
\item Japanese users who are similar to English users are selected. 
\item Items that the Japanese users bought are recommended to the English users.
(All the items bought by a Japanese user who is the most similar to an English user are recommended 
before any items bought by the Japanese user who is the second-most similar to him/her. 
If the feature values are ratings, items are recommended in order of the ratings for Japanese users.)
\end{enumerate}


\section{Experiments}
\label{Sec:Experiment}

Since the purpose of our research is to recommend items at a Japanese shopping site to non-Japanese buyers, 
we wanted to recommend items that were only sold at a Japanese shopping site to non-Japanese buyers. 
However, we could not automatically evaluate the performance of the recommender system 
using  the customer purchase histories of products that were sold only at a Japanese shopping site 
because there were no customer purchase histories where English users purchased items at a Japanese site. 

Therefore, two kinds of experiment were carried out to evaluate the system. They were (1) two-fold cross validation, where half of the translation pairs were masked and (2) experiments in which all the translation pairs were used. 
The former experiment assesses the general performance of the recommender system. 
In contrast, the latter experiments show what kinds of items were recommended in a more realistic scenario. 
The system in both sets of experiments recommended items from the Japanese shopping site to English users. 
A bestseller recommender system, in which the bestsellers at a Japanese shopping site were recommended in order of the number of times they were sold, was implemented and compared with the proposed method. 
 The frequency with which an item was sold was calculated using all the customer purchase histories at the Japanese shopping site.\footnote{The bestseller baseline emphasizes popular products. We used this baseline, although it is unknown whether the data we used contain many of such products. This is because we think that our system can at least recommend items to those who purchase items that are not popular products.}
The Japanese customer purchase histories we used in these two kinds of experiments are summarized in Table \ref{Tab:The num of transactions and users}. 
In the experiments, if a Japanese product name had multiple translations, we used all the translations. 
The items that were in the input vector were not recommended because we believe that most users do not purchase items that they already have. 


\subsection{Cross Validation of Translation Pairs}

In this experiment, two-fold cross validation, where half of the translation pairs are masked, was conducted. 
The items whose translation pair was masked were assumed to be the items sold only at a Japanese site. 
Only common items were used both in Japanese and English customer purchase histories because the other items on the two sites are too different from each other 
to assess the system using cross validation. 
The experiment consisted of five steps. 

\begin{enumerate}
\item Only common items were collected for the experiment. 
\item Half of the translation pairs were masked for the recommendation phase.\footnote{Note that not the customer buying histories but the translation pairs were divided for cross validation.} 
\item The common items with masked translations were masked for the test with respect to the English vectors. 
\item The system recommended items at a Japanese shopping site to English users. 
\item The system then checks if the recommended items have been bought by the English user using the uncovered translation pairs. 
\end{enumerate}

The system recommended not only items with masked translation pairs but also items with unmasked translation pairs. 
In addition, the translation pairs were divided into two groups without looking for the list of common items.  Therefore, the number of masked items differ in the two validations. 
The item-based precisions and recalls at 1\footnote{The system recommended one item for one masked item.} (one recommended item per item) 
and at 10\footnote{The system recommended ten items for one masked item.} (10 recommended items per item), the user-based precisions at 1 and at 10, and the MRRs\footnote{The system recommended any number of items for one masked item.} of the system were evaluated 
to assess the general performance of the recommender system.
As the number of masked items differed 
in the two validations, micro-averaged precisions and recalls were assessed and F-measures 
were calculated based on them. However, macro-averaged MRRs were assessed because it is a criterion about ranks. 


\subsection{More Realistic Scenario}

This experiment shows what kind of items were recommended in a more realistic scenario using all the translation pairs. 
Therefore, not only common items but also the items bought only by Japanese buyers were recommended. 
We present the examples and discuss them in Section \ref{Sec:Disscusion} because the items only sold at a Japanese shopping site could not be automatically checked.

In addition, 
\pagebreak
common items in English vectors were masked for the test and item-based precisions at 1 and recalls were evaluated for reference.\footnote{These experiments were conducted not to assess the performance of the system but to show some examples in a more realistic scenario.} 
Cross validation of the items in the English vectors was conducted; 
half of the common items were masked and checked to determine if they were recommended by the system. 
Binary purchase values were used in this experiment. 
Two methods were used for the evaluations: strict and lenient evaluation methods. 
The system output is only correct in the strict evaluation when the masked item was recommended to users who bought it. 
However, the system output is correct in the lenient evaluation when the recommended item was bought by any of the users in the test set.


\begin{table}[b]
\hangcaption{Item-based precision, recall, and F-measure of experiment with cross validation of the translation pairs}
\label{Tab:Item-based Eval CV}
\input{03table02.txt}
\end{table}


\section{Results}
\label{Sec:Result}

Tables \ref{Tab:Item-based Eval CV}, \ref{Tab:User-based Eval CV}, and  \ref{Tab:MRR} summarize item-based precisions, recalls, and F-measures, 
user-based precisions, and MRRs of experiments with cross validation of translation pairs.
In addition, Table \ref{Tab:Eval real} shows item-based precisions, recalls, and F-measures of experiment in realistic scenario. 
``$-$'' and ``+'' in the ``Format'' column means that the recommendations were performed without and with formatting, respectively. 
``Binary'' and ``value'' in the Feature column means that the feature values for the recommendations were binary or rating values. 
``CF'' in the tables means collaborative filtering. 
``1'' and ``10'' in the ``N of Answers'' column means that the results at 1 and 10, respectively.
Bestseller (English) in Table \ref{Tab:Eval real} means the case that only English items recommended. 
(This system could not recommend items sold only at a Japanese site.)

\begin{table}[t]
\caption{User-based precision of the experiment with cross validation of the translation pairs}
\label{Tab:User-based Eval CV}
\input{03table03.txt}
\end{table}
\begin{table}[t]
\caption{MRR and rank of the experiment with cross validation of the translation pairs}
\label{Tab:MRR}
\input{03table04.txt}
\end{table}
\begin{table}[t]
\caption{Item-based precision, recall, and F-measure of the experiment in a realistic scenario}
\label{Tab:Eval real}
\input{03table05.txt}
\end{table}

We defined the item-based precision, user-based precision, and recall as follows:
{\allowdisplaybreaks
\begin{gather}
\mathit{Precision}_\mathit{item}=\frac{N_\mathit{item}}{Np_\mathit{item}}
\label{eq:1} \\[1ex]
\mathit{Precision}_\mathit{user}=\frac{N_\mathit{user}}{Np_\mathit{user}}
\label{eq:2} \\[1ex]
\mathit{Recall}_\mathit{item}=\frac{N_\mathit{item}}{Nr}
\label{eq:3}
\end{gather}
}
where  $N_\mathit{item}$, $Np_\mathit{item}$, $N_\mathit{user}$, $Np_\mathit{user}$, and $Nr$ correspond to the number of correct items output by the system, 
number of items output by the system, 
number of users the correct items are recommended to, 
number of all users to whom items are recommended by the system, 
and number of all masked items. 
Here, $\frac{1}{k}$ is added to $N_{user}$ when one item is correct ($k$ is the number of items that are masked for the user in question). 
Thus, user-based precision can be 100\% only when all the items of all the users are correctly recommended. 

In addition, 
MRR was calculated as: 
\begin{equation} 
MRR = \frac{1}{N} \sum^{N}_{i=1}\frac{1}{\mathit{rank(i)}}
\label{eq:mrr}
\end{equation}

where $N$ denotes the number of test data (users) and $\mathit{rank}(i)$ denotes the highest ranking of the correct products that are recommended to user $i$. 
When none of the items recommended to user $i$ are correct, $\mathit{rank}(i)$ is zero.

The rank of Table \ref{Tab:MRR} shows the rank of the correct items that are generally recommended.


\section{Discussion}
\label{Sec:Disscusion}

\subsection{Cross Validation of Translation Pairs}

Table \ref{Tab:Item-based Eval CV} shows that the collaborative filtering system outperformed the bestseller recommender system 
with respect to all criteria except for recall at 10 when item-based precisions, recalls, and F-measures are compared.  In addition, although the recalls at 10 of bestseller recommender system (27.80\% without formatting and 33.19\% with formatting) substantially outperformed those of the collaborative filtering system (0.15\% and 0.20\% without formatting and 7.98\% and 7.97\% with formatting), 
this is only because the bestseller recommender system  recommended many items; it recommended more than 34 times (without formatting) or seven times (with formatting) 
as many items as the collaborative filtering system recommended. 
Table \ref{Tab:Item-based Eval CV} also shows that formatting makes the results better regardless of the method. 
In addition, the results improved with respect to all criteria except for the precision of the collaborative filtering system when the number of recommended items was increased from 1 to 10. The results are almost the same as for the precisions of the collaborative filtering system. Moreover, the results are almost identical to each other when the feature values were binary or ratings. 

Table \ref{Tab:User-based Eval CV} shows that the bestseller recommender system was better in the use-based precision at 1 
when the titles were not formatted,\footnote{The results are very low.} 
but the collaborative filtering system was better when the titles were formatted. 
The same table shows that the bestseller recommender system was better at user-based precision at 10 regardless of formatting. 
The collaborative filtering system with formatting was the best when the user-based precisions at 1 are compared and the bestseller system was the best when the user-based precisions at 10 are compared. 
These results indicate that the collaborative filtering system recommended fewer correct products but did so more quickly. 
In addition, the results are almost identical to each other when the feature values were binary or rating values. 

Moreover, Tables \ref{Tab:Item-based Eval CV} and \ref{Tab:User-based Eval CV} show that the collaborative filtering system was better for the item-based precision at 10 
but the bestseller system was better for the user-based precision at 10. 
These results indicate that the collaborative filtering system was better for the user who bought many items and that the bestseller system tends to recommend the correct products more for the user who buys fewer items. 
The original collaborative filtering has a cold start problem and this is also the case here.


Finally, Table \ref{Tab:MRR} show that, as for MRR, the collaborative filtering system outperforms the bestseller system when the titles are formatted. 
In addition, formatting makes the results better regardless of the method. Moreover, the results are almost identical to each other yet again whether the feature values were binary or ratings. 
The collaborative filtering system with formatting when binary values were used was the best system. 
Table \ref{Tab:MRR} shows that for the 6.43th item, the collaborative system recommends was generally correct although for the 17.12th item the bestseller recommender system recommends is generally correct. 
This indicates that the bestseller recommender system has to wait more than twice as long as the collaborative filtering system to make a sale. 


\subsection{More Realistic Scenario}

Table \ref{Tab:Eval real} shows that the performance of the collaborative filtering system is low.
In particular, the low values for lenient evaluations of the recommendations from the Japanese shopping site to English users indicate how different they are. 
The reason for the poor performances of recommendations from the Japanese shopping site is the difference in the items that the two shopping sites sell. 

However, none of the items the bestseller recommender system recommended were bought by any English users. 
In addition, not only the strict precision and recall but also those of the lenient evaluation were 0\%. 
Moreover, the bestseller recommender system could correctly  recommend only 45 items even if the system recommended only items with translation pairs.   
These results reveal that naive collaborative filtering could better recommend items that the English buyers bought than the bestseller recommender system.

Since the purpose of our research was to recommend items at a Japanese shopping site to non-Japanese buyers, the system is successful if it can recommend products that seem to be related with buyers' interests at only the Japanese site, even if the precision or recalls are low. 

Therefore, we finally present examples of the recommender system outputs. 
For users who bought {\it Fantasia}, {\it Wizard of Oz, The}, {\it Pinocchio}, {\it Alice in Wonderland}, {\it Snow White and the Seven Dwarfs}, {\it Beauty and the Beast}, and {\it Cinderella}, 
the system recommended {\it The Aristocats}, {\it The Little Mermaid}, {\it Dumbo}, ねむれるもりのびじょ({\it Sleeping Beauty}), {\it Alice's Adventures in Wonderland}, 
101 ぴきわんちゃん({\it 101 Dalmatians}), しらゆきひめ({\it Snow White}), 風の谷のナウシカ7 巻セット({\it Nausica\"a of the Valley of the Wind Set: Volumes 1 to 7}), 
{\it Peter Pan}, {\it Toy Story 3}, and プーさんをさがせ({\it Look and Find Pooh}). 
Here, the English names were the products that had translation pairs and the Japanese names were those that did not have them. 
In addition, バック・トゥ・ザ・フューチャーPart 3 ({\it Back to the Future Part 3}) or バックトゥザフューチャー25th アニバーサリー({\it Back to the Future 25th Anniversary}) 
were recommended to users who bought {\it Back to the Future}. 
These results indicate that even if the recommended items were determined to be incorrect by the system, they included many correct answers.

In addition, {\it Nausica\"a of the Valley of the Wind Set: Volumes 1 to 7} and 
{\it Back to the Future 25th Anniversary} were products that were sold only at the Japanese shopping site. 
They also seemed to be related to buyers' interests. 
Therefore, we think that simple collaborative filtering between two shopping sites in different languages could provide many effective recommendations.


These items could not be recommended by the bestseller recommender system at all.\footnote{For example, the top-5 bestsellers were 1. {\it One Piece} (Japanese manga), 2. {\it This Is It} (CD) 3. {\it Cath Kidston “HELLO！” FROM LONDON}, 4. 鋼の錬金術師 (Japanese manga, English title is {\it Fullmetal Alchemist}), and 5. 体脂肪計タニタの社員食堂 (a Japanese cookbook). }

However, {\it Back to the Future Part 3} was also sold at the English shopping site, as was {\it Back to the Future Part III}. 
The system could not identify that they were the same product. We think that more accurate translation pairs will lead to fewer errors of that nature. Formatting using the edit distance was tried, but it increased erroneous translations: 天使と悪魔({\it Angels \& Demons}) was identified as 
天使と小悪魔 (literally meaning angels and demons, but they are totally different work). 
More precise formatting of product names should be investigated in the future.

Finally, collaborative filtering has some limitations such as sparsity, scalability, and synonymy. 
\cite{Sarwar} used singular value decomposition to reduce
 dimensionality. We think that this technique could be used together with our method because it is effective in some domains. 
In addition, we plan to perform domain adaptation in a scenario in which we use a small number of English customer purchase histories in addition to Japanese customer purchase histories in the future. 


\section{Conclusion}
\label{Sec:Conclusion}

We developed a system that recommends products at a Japanese shopping site to non-Japanese users using naive collaborative filtering. The same items on both Japanese and English sites were identified using translation pairs because the items had different product names according to the language. Our experiments reveal that the collaborating filtering system outperformed the bestseller recommender system. They also reveal that products only at the Japanese site that seemed to be related to the buyers' interests could be found by the system. In addition, even if the recommended items were determined to be incorrect by the system, they included many answers that seemed to be correct. We think that simple collaborative filtering between two shopping sites in different languages could provide many effective recommendations. 


\section*{Acknowledgment}

This paper is the revised version of \cite{cicling}, which is published in the proceedings of the CICLING 2014. 
We would like to thank Rakuten, Inc. and the National Institute of Informatics which provide us the ``Rakuten Data Release.'' This work was partially supported by JSPS KAKENHI Grant Number 15K16046. 


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\begin{biography}
\bioauthor[:]{Kanako Komiya}{
received her Ph.D. degree in Tokyo University of Agriculture and Technology in 2009. After being a postdoctoral fellow in Tokyo Institute of Technology and an assistant professor in Tokyo University of Agriculture and Technology, she is currently a lecturer in Ibaraki University. She is interested in natural language processing. She is a member of IPSJ, JSAI, and ANLP.
}

\bioauthor[:]{Minoru Sasaki}{
received his Ph.D. degree in Tokushima University. After being a research assistant in Ibaraki University, he is currenly a lecturer in Ibaraki University. His research interests include information retrieval and natural language processing. He is a member of IPSJ and ANLP.
}

\bioauthor[:]{Hiroyuki Shinnou}{
received his Ph.D. degree in Tokyo Institute of Technology. After being a researcher in Fuji Xerox Co., Ltd. and Panasonic Corporation and a research assistant, a lecturer, and an associate professor in Ibaraki University, he is currently a professor in Ibaraki University. His research interests include Bayes statistics, machine learning and natural language processing. He is a member of IPSJ, JSAI, and ANLP.
}

\bioauthor[:]{Yoshiyuki Kotani}{
received his Ph.D. degree in the University of Tokyo. He is currently a professor emeritus at the Tokyo University of Agriculture and Technology. His research interests include artificial intelligence, game programming, and natural language processing. He is a member of IPSJ and JSAI.
}
\end{biography}


\biodate



\end{document}
