1 research outputs found
An Unbiased Data Collection and Content Exploitation/Exploration Strategy for Personalization
One of missions for personalization systems and recommender systems is to
show content items according to users' personal interests. In order to achieve
such goal, these systems are learning user interests over time and trying to
present content items tailoring to user profiles. Recommending items according
to 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, only
leaving partial feedbacks to the system to learn in the next cycle, which leads
to significant biases into systems and hence results in a situation where user
engagement metrics cannot be improved over time. The problem is not just for
one component of the system. The data collected from users is usually used in
many different tasks, including learning ranking functions, building user
profiles and constructing content classifiers. Once the data is biased, all
these downstream use cases would be impacted as well. Therefore, it would be
beneficial to gather unbiased data through user interactions. Traditionally,
unbiased data collection is done through showing items uniformly sampling from
the content pool. However, this simple scheme is not feasible as it risks user
engagement metrics and it takes long time to gather user feedbacks. In this
paper, we introduce a user-friendly unbiased data collection framework, by
utilizing methods developed in the exploitation and exploration literature. We
discuss how the framework is different from normal multi-armed bandit problems
and why such method is needed. We layout a novel Thompson sampling for
Bernoulli ranked-list to effectively balance user experiences and data
collection. The proposed method is validated from a real bucket test and we
show strong results comparing to old algorithm