Yamashita Haruka

Recently, there are various methods for activating data that is accumulated by e-commerce sites for the improvement the service quality. In this paper, we focus on the recommendation system and describe an approach, experiments, and future issues of the research that the author and students in the author’s laboratory have developed to improve the quality of service for a specific e-commerce site.

Online coupon distribution is a significant marketing measure that leads to increased sales. However, distributing coupons blindly risks lowering a company's profit ratio. It is, therefore, essential to estimate the coupon effect. In addition, users' potential purchase intention is thought to make a difference in the coupon effect. For example, users with low purchase intentions are likely to increase their gross profit through coupons. In contrast, users with high purchase intentions will likely decrease their gross profit through coupons. Therefore, it is possible to conduct highly effective targeting by analyzing the relationship between potential purchase intention and the coupon effect. In this study, we propose a framework containing an experimental design and a verification method based on machine learning to analyze the relationship between the coupon effect and the user's potential purchase intention. Finally, we demonstrate the effectiveness of the proposed framework by applying it to real-world data.

In the field of conventional recommendation systems, most of the models have been based on the prediction of evaluation values using evaluation value data directly assigned by users to their satisfaction with items. Recently, recommendation models that utilize behavioral history data such as implicit evaluation have been widely used. Neural Collaborative Ranking is a method for estimating and ranking the next most likely items to be observed in the list of items. Whereas, there are cases in which multiple implicit evaluations at different levels are observed, such as purchasing and browsing. However, the conventional NCR model cannot distinguish and learn multiple implicit evaluations, and cannot fully utilize the observed data. Therefore, in this study, we propose a model that takes into account multiple implicit evaluations with different levels in the NCR by adopting the method of Ding et al. In addition, we demonstrate the effectiveness of the proposed method.

Several studies have been proposed to analyze the factors that cause stock price fluctuations. It's known that utilizing textual data is effective in building models. In order to utilize text data in this method, it's necessary to quantify sentences using bag-of-words, etc., and at that time, concrete information is abstracted. Therefore, when it comes to investigating what specific factors caused the stock price fluctuation at a certain point in time, it's inappropriate to do so. In this case, it's desirable to construct a model using data other than textual data first, and then clarify the textual information to be focused on from the model. In this study, I first build a time-varying coefficient model using numerical data. Then, by focusing on the residuals of the model, we found the relationship between stock price fluctuations and specific factors expressed in text data at surrounding points in time.

In recent years, many images and texts related to fashion coordination have been posted on social media services. It has become common for users to select their outfits based on other users' posts. Social media services store a large amount of user, image, linguistic information, etc., that are expected to be an advantage in business by the appropriate utilization. In this study, we propose a recommendation method for fashion coordination posts based on both image and linguistic similarity. Specifically, at first, Image2StyleGAN is learned to measure the image similarity, and Doc2Vec is learned to measure the linguistic information similarity. Secondly, we obtain the two types of similarity rankings by the above methods and calculate their weighted sum with an adjustable parameter. This allows us to customize which information is more important for each user. Finally, we present how to utilize the proposed method using real-world fashion coordination service application data.

There have been many cases of AIs defeating human world champions in perfect information games.However,since these AIs seek pure strength,it is difficult for inexperienced players to enjoy the game.Therefore,we propose an AI that matches the abilities of the opposing players and makes the game competitive.We propose an algorithm based on Monte Carlo Tree Search (MCTS).In previous Othello AI,the difficulty level was set based on the adjustment of hyper-parameters that increase the winning rate in relation to the objective function of the algorithm.This will create an AI in which matches are more likely to be draws,and is expected to create an AI that can be enjoyed by players of a wide range of levels.We will evaluate the validity of the proposed model by comparing the performance of the proposed model against each of the AI of a certain level.

In this study,we propose a model that appropriately predicts the probability that a horse will win and a method for betting on horses with the highest possible return based on the horse's odds information.Specifically, since horse racing data is a mixture of time series data and attribute data,we use a partially recurrent neural network,which can appropriately predict the odds of a horse winning,to predict the odds of a horse winning.Furthermore,we propose a method to search for the optimal betting strategy while minimizing the loss by not only predicting the winning rate but also betting based on the expected value of the odds.Finally,we simulate two types of betting,the conventional method and the proposed method,using actual horse racing data,where the winning probability is the probability of finishing in the top three positions.

In question-and-answer sessions on Q&A services, it is sometimes difficult to read a long or poorly-written question and answer. In such cases, if the system can recommend appropriate images for the questions, it can assist reading comprehension based on the information in the images. In this study, we propose a machine learning model for recommending appropriate images for questions in a Q&A service using Sentence-BERT (SBERT). Specifically, the model achieves this by converting question sentences and image captions into a vector using SBERT, measuring the cosine similarity between them, and recommending the image with the caption that has the maximum value. From a practical point of view, it is also necessary to minimize inappropriate recommendation results when SBERT malfunctions. Therefore, in order to ensure that the recommended images are at least correctly recommended from the categorical point of view, a categorization model based on BERT's transfer learning is applied as an auxiliary. This is achieved by classifying the recommended images into categories that exist in each Q&A service and performing SBERT and cosine similarity measures within each category.

Internet advertising expenditures have been increasing in recent years, and the market is expected to grow in the future. Among these, most of the market is dominated by managed advertising, which enables targeting based on consumer attributes and the determination of advertising distribution media. Therefore, it would be a great advantage for companies if they can clarify the optimal advertisement delivery destination for each consumer and improve the advertising effectiveness of managed advertisements. In this study, we construct a model for estimating the optimal Internet ad serving media using a hierarchical Bayesian model that enables flexible model construction that considers differences in consumer attributes. Based on the assumption that the distribution media with the greatest advertising effectiveness differs depending on consumer attributes, we propose an analytical model that introduces a hierarchical Bayesian framework. First, the hierarchical Bayesian model is used to analyze the relationship between consumer attributes, changes in purchase intention for a given product, and the frequency of use of each Web medium. Furthermore, the obtained parameter values are used to calculate the effectiveness of each medium in terms of the attributes of the target consumer, and the optimal destination of advertisements is determined.

SNSの普及により膨大なデータが蓄積され，マーケティング施策を立案するためにユーザの様々な分析が行われている.本研究では年齢や用途の違いなどSNSを用いるユーザの多様性．に着目し，当てはまりがよくかつ解釈性の高い手法の提案を目的とする．これまで，クラスタリングと重回帰分析を同時に行うことで属性に合わせた分析を可能とするクラスタワイズ回帰分析に関する研究が数多く展開されてきた．本研究では，まずこのアルゴリズムにおける初期値依存性を指摘し，解消しうるモデルを提案する．さらに，提案したアルゴリズムの回帰部分を機械学習に置き換え，解釈をする方法を導入することで，当てはまりおよび解釈性に優れた手法を提案する．

近年、さまざまな分野に画像生成の技術が応用されていて、ファッション業界への応用に関する研究も展開されている。先行研究では、指定した洋服やポーズをもとにファッション画像を生成したり、画像に写っている人物の洋服を変化させることに焦点が当てられている。しかし、実際のビジネスのサービスにおいて、ユーザー一人一人に合わせた画像生成を実現する場合に、洋服やポーズだけを考慮するのでは対象のユーザーの属性に合わない画像を生成してしまう恐れがある。そこで本研究では、画像の人物の属性情報を利用することを考える。さらに、いいね数という画像の評価データを利用し、ConditionalStyleGAN2-adaに当てはめることで、ユーザー一人一人に合わせた高評価画像の生成ができることを示す。

アフターコロナの現在，スキンケア需要が高まっており，スキンケアブランドが新規顧客を獲得する好機となっている．また，顧客にブランドの良さを理解してもらい，ロイヤルティを高められれば，将来的にブランドを支える優良顧客へと成長させることができる．現在，データとして取得できる情報は既存の顧客のみと限定されている．本研究では既存顧客のアンケートに基づきロイヤルユーザの特徴を明らかにし，ロイヤルユーザになる見込みが高い新規顧客の獲得方法を検討する．その際，多様な価値観や属性情報を考慮しうる確率的潜在クラスモデルの考え方を導入した決定木モデルを提案し，実際のデータを用いてマーケティング施策について考察する．

宮古島のマンゴーを用いて官能実験を行う場合、大量のマンゴーを用意することが難しい。またcovid-19の影響により多くの人々を集めて行う大規模実験は難しいため、効率の良い実験方法としていくつかの手法は提案されている。本研究ではこれに対して官能実験において評価項目が多く被験者の負担も大きいという問題に着目して、実験の1人当たりの負荷を減らすために実験結果の分散を活用した実験の削減方法を考える。具体的には、いくつかの制約の基で最適な実験の削減を数理最適化のアプローチから探索し、さらに様々な方法で欠損値を補完することを検討し、負担の軽減を実現する最適な方法を提案する。

宮古島産マンゴーは経日による品質劣化が大きな問題となっている。これまで温泉水による処理が有効なアプローチとして注目されており、成分実験による有効性が示されているものの、官能実験に基づく有効性は十分には示されていない。また、コロナウィルスの影響で多くの被験者を集めた実験は困難であるため、大規模な実験が困難である。そこで本研究では、どのような人が宮古島産マンゴーにどのような評価をするか明らかにし、ターゲティングを考察することを目的とする。具体的には、直行配列表を用いた実験計画法により被験者を削減し、疑似官能データを生成すると同時に転移学習型NMFに基づき実際の観測値を活用した疑似官能データを分析する。

近年、野球の試合をより有利に進めるために、様々なアプローチが提案されている。打者にとって難しい意思決定として、初球を積極的に振っていくのか、慎重に見極めるべきなのかが挙げられる。本研究では、打者にとっての初球へのアプローチを得点の観点から最大化するために、時系列データを含む複雑な入出力データの関係を精度よく推定しうる分析モデルを構築、モデルを基に最適な選択を決定する方法を提案する。具体的には、ピッチャーの配球を時系列データとしてとらえ、かつその状況を考慮した精度の良い予測をする部分再帰型ニューラルネットワークを推定し、その結果を用いて初球を積極的に打つべきなのかについて最適化する。

Collaborative Metric Learning (CML) is a recommendation model based on implicit data, i.e. behavior history such as clicks and browsings. CML learns an metric space to embed not only the relationship between users and items, but also the similarity between items and that between users. Moreover, CML recommends the items which are close to each user in the trained embedding space. However, CML tends to learn by focusing on items that are popular among many users, and the accuracy of embedded representations of other minor items is often neglected. On the other hand, it is necessary to learn embedded representations of many minor items that match the user's preferences with high accuracy in order to provide unexpected recommendations that users may not have recognized. In this study, we propose a method to learn the embedded representations that capture user's preferences by weighting according to the number of observations of implicit data, and to make unexpected recommendations that include minor items. Finally, we apply the proposed method to actual movie evaluation data set, and show the usefulness of the proposed method in making unexpected recommendations based on the users' preferences.