Recently online advertisers utilize Recommender systems (RSs) for display
advertising to improve users' engagement. The contextual bandit model is a
widely used RS to exploit and explore users' engagement and maximize the
long-term rewards such as clicks or conversions. However, the current models
aim to optimize a set of ads only in a specific domain and do not share
information with other models in multiple domains. In this paper, we propose
dynamic collaborative filtering Thompson Sampling (DCTS), the novel yet simple
model to transfer knowledge among multiple bandit models. DCTS exploits
similarities between users and between ads to estimate a prior distribution of
Thompson sampling. Such similarities are obtained based on contextual features
of users and ads. Similarities enable models in a domain that didn't have much
data to converge more quickly by transferring knowledge. Moreover, DCTS
incorporates temporal dynamics of users to track the user's recent change of
preference. We first show transferring knowledge and incorporating temporal
dynamics improve the performance of the baseline models on a synthetic dataset.
Then we conduct an empirical analysis on a real-world dataset and the result
showed that DCTS improves click-through rate by 9.7% than the state-of-the-art
models. We also analyze hyper-parameters that adjust temporal dynamics and
similarities and show the best parameter which maximizes CTR.

本文提出了动态协同过滤汤姆逊取样模型 (DCTS)，通过用户和广告之间的相似性来估计 Thompson 取样的先验分布，并将知识在多个领域之间进行转移。实证分析结果表明，DCTS 比现有模型将点击率提高了 9.7%