Transferable Contextual Bandit for Cross-Domain Recommendation

By Bo Liu, Ying Wei, Yu Zhang, Zhixian Yan, Qiang Yang. 2018

59 citations recorded by the source corpus.

Traditional recommendation systems (RecSys) suffer from two problems: the exploitation-exploration dilemma and the cold-start problem. One solution to solving the exploitation-exploration dilemma is the contextual bandit policy, which adaptively exploits and explores user interests. As a result, the contextual bandit policy achieves increased rewards in the long run. The contextual bandit policy, however, may cause the system to explore more than needed in the cold-start situations, which can lead to worse short-term rewards. Cross-domain RecSys methods adopt transfer learning to leverage prior knowledge in a source RecSys domain to jump start the cold-start target RecSys. To solve the two problems together, in this paper, we propose the first applicable transferable contextual bandit (TCB) policy for the cross-domain recommendation. TCB not only benefits the exploitation but also accelerates the exploration in the target RecSys. TCB's exploration, in turn, helps to learn how to transfer between different domains. TCB is a general algorithm for both homogeneous and heterogeneous domains. We perform both theoretical regret analysis and empirical experiments. The empirical results show that TCB outperforms the state-of-the-art algorithms over time.

Subfields: Electrical and Electronic Engineering, Information Systems, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Yu Zhang
2. Qiang Yang
3. Dongrui Wu
4. Vladlen Koltun
5. Ozan Sener

In Information Systems

1. Qiang Yang
2. Guangneng Hu
3. Yu Zhang
4. Weike Pan
5. Sotirios P. Chatzis
6. Jie Lu
7. Yaochu Jin
8. Qian Zhang

In Management Science and Operations Research

1. Qiang Yang
2. Guangneng Hu
3. Yu Zhang

Citations in the corpus

• CoNet: Collaborative Cross Networks for Cross-Domain Recommendation (2018, methodology): “Some other shallow methods exploit the bandit learning [23] and the graph-based approach [41].”
• Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text (2019, unknown): “com/en-us/) [30].”