Accurate parameter estimation based on latent class model estimated by combining both evaluation and purchase histories

Takahiro Oi, Kenta Mikawa, Masayuki Goto

    Research output: Contribution to journalArticle

    Abstract

    Recently, the recommender system which automatically recommends product items for customers has become more important as an efficient web marketing tool. In many electronic commerce (EC) sites, the data of customers' purchases and evaluation histories are stored in a database. By using them, the system predicts user's preferences, and automatically recommends items that seem to be preferred, but have not been purchased yet. In this study, we focus on the recommender system based on collaborative filtering (CF) with a latent class model. CF recommends items by using purchase or evaluation history data. Considering real purchase activity on EC sites, most of the consumers who bought items on an EC site don't post their evaluation on the site. That means more purchase history data is stored more in the database than evaluation history data. However, most studies of CF used only evaluation data to learn the model. In this case, the purchase data is not used to construct a model even though its data size is much larger than that of evaluation history. It is more desirable to learn a model by using not only evaluation history, but also purchase data to improve the CF accuracy. The purpose of this study is to construct an effective CF model to improve the CF accuracy by formulating the estimation using both evaluation history data and abundant purchase history data which has not been used in previous CF studies. Specifically, we use the aspect model, which is one of latent class models of CF. We propose a way to estimate its parameters using both evaluation history and purchase data. To verify the effectiveness of this study, a simulation experiment was conducted using a bench mark data of recommender system. We show that the prediction accuracy of the recommendation is improved.

    Original languageEnglish
    Pages (from-to)286-293
    Number of pages8
    JournalJournal of Japan Industrial Management Association
    Volume65
    Issue number4
    Publication statusPublished - 2015

    Fingerprint

    Latent Class Model
    Collaborative filtering
    Parameter estimation
    Parameter Estimation
    Collaborative Filtering
    History
    Evaluation
    Recommender systems
    Electronic commerce
    Recommender Systems
    Electronic Commerce
    Latent class model
    Purchase
    Customers
    Marketing
    Model
    User Preferences
    Simulation Experiment
    Recommendations

    Keywords

    • Aspect model
    • EM algorithm
    • Latent class model
    • Parameter estimation
    • Recommender system

    ASJC Scopus subject areas

    • Industrial and Manufacturing Engineering
    • Applied Mathematics
    • Management Science and Operations Research
    • Strategy and Management

    Cite this

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    abstract = "Recently, the recommender system which automatically recommends product items for customers has become more important as an efficient web marketing tool. In many electronic commerce (EC) sites, the data of customers' purchases and evaluation histories are stored in a database. By using them, the system predicts user's preferences, and automatically recommends items that seem to be preferred, but have not been purchased yet. In this study, we focus on the recommender system based on collaborative filtering (CF) with a latent class model. CF recommends items by using purchase or evaluation history data. Considering real purchase activity on EC sites, most of the consumers who bought items on an EC site don't post their evaluation on the site. That means more purchase history data is stored more in the database than evaluation history data. However, most studies of CF used only evaluation data to learn the model. In this case, the purchase data is not used to construct a model even though its data size is much larger than that of evaluation history. It is more desirable to learn a model by using not only evaluation history, but also purchase data to improve the CF accuracy. The purpose of this study is to construct an effective CF model to improve the CF accuracy by formulating the estimation using both evaluation history data and abundant purchase history data which has not been used in previous CF studies. Specifically, we use the aspect model, which is one of latent class models of CF. We propose a way to estimate its parameters using both evaluation history and purchase data. To verify the effectiveness of this study, a simulation experiment was conducted using a bench mark data of recommender system. We show that the prediction accuracy of the recommendation is improved.",
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