In this post, I will present some benchmark datasets for recommender system, please note that I will only give the links of those datasets. Courtesy of entaroadun. A Kaggle dataset for Criteo display advertising challenge. Criteo is a personalized retargeting company that works with Internet retailers to serve personalized online display advertisements to consumers. The goal of this Kaggle challenge is to predict click-through rates on display ads. In the labeled training set over a period of 7 days, each row corresponds to a display ad served by Criteo.
Network embedding or graph embedding has been widely used in many real-world applications. Ranked 1 on Link Prediction on Amazon. Recommender Systems are becoming ubiquitous in many settings and take many forms, from product recommendation in e-commerce stores, to query suggestions in search engines, to friend recommendation in social networks. Product Recommendation Recommendation Systems. Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to accumulate new information that may improve future performance.
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