Development of an offline OOH advertising recommendation system using negative sampling and deep interest network

The out-of-home (OOH) advertising market has been operated exclusively following the know-how of salespeople. Thus, it is difficult to make scientific decisions and systematically provide various options to advertisers. In this regard, this study develops an OOH advertising recommendation system by analyzing past OOH history data. The OOH advertising allocation problem has the characteristics that the training data are implicit feedback, and only one advertisement can be posted per offline billboard. This study proposes a recommendation system suitable for OOH history data using negative sampling and Deep Interest Network. The proposed recommendation system showed a higher performance than excisting models used for comparison purposes, and the findings of this study present implications for solving similar recommendation problems.