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We present a novel recommender systems dataset that records the sequentialinteractions between users and an online marketplace. The users aresequentially presented with both recommendations and search results in the formof ranked lists of items, called slates, from the marketplace. The datasetincludes the presented slates at each round, whether the user clicked on any ofthese items and which item the user clicked on. Although the usage of exposuredata in recommender systems is growing, to our knowledge there is no openlarge-scale recommender systems dataset that includes the slates of itemspresented to the users at each interaction. As a result, most articles onrecommender systems do not utilize this exposure information. Instead, theproposed models only depend on the user's click responses, and assume that theuser is exposed to all the items in the item universe at each step, oftencalled uniform candidate sampling. This is an incomplete assumption, as ittakes into account items the user might not have been exposed to. This wayitems might be incorrectly considered as not of interest to the user. Takinginto account the actually shown slates allows the models to use a more naturallikelihood, based on the click probability given the exposure set of items, asis prevalent in the bandit and reinforcement learning literature.\cite{Eide2021DynamicSampling} shows that likelihoods based on uniformcandidate sampling (and similar assumptions) are implicitly assuming that theplatform only shows the most relevant items to the user. This causes therecommender system to implicitly reinforce feedback loops and to be biasedtowards previously exposed items to the user.

FINN.no Slates Dataset: A new Sequential Dataset Logging Interactions, allViewed Items and Click Responses/No-Click for Recommender Systems Research