Interaction data is the most simple indication of users’ possibilities andinterests. It plays a critical role in former introduced models. Yet,interplay data is typically extremely sparse and may be noisy every now and then. To tackle this issue, we can integrate side assistance such asfeatures of items, profiles of users, and even in which context that theinteraction occurred into the advice model. Utilizing thesefeatures are constructive in making strategies in that these featurescan be a solid predictor of users interests especially wheninteraction data is lacking.
As such, it is a must-have for recommendationmodels also have the capability to handle those features and provides themodel some content/context recognition. To reveal this type ofrecommendation models, we introduce an alternative task on click via rateCTR for online advertisement strategies and latest an anonymousadvertising data. Targeted commercial facilities have attractedwidespread attention and are sometimes framed as recommendation engines. Recommending adverts that match users’ personal taste andinterest is vital for click through rate improvement. With the abundant developments of Internet and mobile generation,online advertising has become an important income resource and generatesvast majority of earnings in the Internet industry. It is important todisplay applicable adverts or advertisements that pique users’interests so that casual company can be converted into payingcustomers.
The dataset we announced is a web advertisements dataset. It contains 34 fields, with the 1st column representing the targetvariable that suggests if an ad was clicked 1 or not 0. All theother columns are categorical aspects. The columns might constitute theadvertisement id, site or program id, device id, time, user profilesand so on. The real semantics of the elements are undisclosed due toanonymization and privacy concern.