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One of missions for personalization systems and recommender systems is toshow content items according to users' personal interests. In order to achievesuch goal, these systems are learning user interests over time and trying topresent content items tailoring to user profiles. Recommending items accordingto users' preferences has been investigated extensively in the past few years,mainly thanks for the popularity of Netflix competition. In a real setting,users may be attracted by a subset of those items and interact with them, onlyleaving partial feedbacks to the system to learn in the next cycle, which leadsto significant biases into systems and hence results in a situation where userengagement metrics cannot be improved over time. The problem is not just forone component of the system. The data collected from users is usually used inmany different tasks, including learning ranking functions, building userprofiles and constructing content classifiers. Once the data is biased, allthese downstream use cases would be impacted as well. Therefore, it would bebeneficial to gather unbiased data through user interactions. Traditionally,unbiased data collection is done through showing items uniformly sampling fromthe content pool. However, this simple scheme is not feasible as it risks userengagement metrics and it takes long time to gather user feedbacks. In thispaper, we introduce a user-friendly unbiased data collection framework, byutilizing methods developed in the exploitation and exploration literature. Wediscuss how the framework is different from normal multi-armed bandit problemsand why such method is needed. We layout a novel Thompson sampling forBernoulli ranked-list to effectively balance user experiences and datacollection. The proposed method is validated from a real bucket test and weshow strong results comparing to old algorithms

An Unbiased Data Collection and Content Exploitation/Exploration Strategy for Personalization