we propose a \textbf{C}ounterfactual \textbf{M}ulti-player \textbf{B}andits (CMB) method to deliver explainable recommendation diversification across a wide range of diversity metrics. Leveraging a counterfactual framework, our method identifies the factors influencing diversity outcomes. Meanwhile, we adopt the multi-player bandits to optimize the counterfactual optimization objective, making it adaptable to both differentiable and non-differentiable diversity metrics.

Yansen Zhang, Bowei He, Xiaokun Zhang, Haolun Wu, Zexu Sun, Chen Ma.

We propose a comprehensive framework, Explainable Operations Research (EOR), emphasizing actionable and understandable explanations accompanying optimization. The core of EOR is the concept of Decision Information, which emerges from what-if analysis and focuses on evaluating the impact of complex constraints (or parameters) changes on decision-making. Specifically, we utilize bipartite graphs to quantify the changes in the OR model and adopt LLMs to improve the explanation capabilities.

Yansen Zhang, Qingcan Kang, Wing Yin Yu, Hailei Gong, Xiaojin Fu, Xiongwei Han, Tao Zhong, Chen Ma.

We propose a Hierarchical Gating Network for Cross-Domain Sequential Recommendation (HGNCDSR). Specifically, we simultaneously train single-domain and cross-domain interaction sequences, utilizing a hierarchical gating network to capture user interest representations in single-domain and cross-domain, respectively. A feature gating and an instance gating are applied respectively to extract user interests at item feature level and instance level.

Shuliang Wang, Jiabao Zhu, Yi Wang, Chen Ma, Xin Zhao, Yansen Zhang, Ziqiang Yuan, Sijie Ruan.

In this survey, we are the first to propose a unified taxonomy for classifying the metrics and approaches of diversification in both search and recommendation, which are two of the most extensively researched fields of retrieval systems.

Haolun Wu*, Yansen Zhang*, Chen Ma, Fuyuan Lyu, Bowei He, Bhaskar Mitra, Xue Liu.

We propose a new CRS framework called Conversational Recommender with Implicit Feedback (CRIF). CRIF formulates the conversational recommendation scheme as a four-phase process consisting of offline representation learning, tracking, decision, and inference. In the inference module, by fully utilizing the relation between users' attribute-level and item-level feedback, our method can explicitly deduce users' implicit preferences.

Chenhao Hu, Shuhua Huang, Yansen Zhang, Yubao Liu.

We proposed a self-adaptive graph neural network with future contexts for sequential recommendation. SA-GNN applied a GNN model within both past and future contexts to model user dynamic interests, and utilized a self-adaptive module and attention module to weaken the negative effect of unsuitable connections in item graph.

Yansen Zhang, Chenhao Hu, Genan Dai, Weiyang Kong, Yubao Liu.