Yinan Zhang

Yinan Zhang, Pei Wang, Congcong Liu et al.

Recently, Graph Convolutional Network (GCN) has become a novel state-of-art for Collaborative Filtering (CF) based Recommender Systems (RS). It is a common practice to learn informative user and item representations by performing embedding propagation on a user-item bipartite graph, and then provide the users with personalized item suggestions based on the representations. Despite effectiveness, existing algorithms neglect precious interactive features between user-item pairs in the embedding process. When predicting a user's preference for different items, they still aggregate the user tree in the same way, without emphasizing target-related information in the user neighborhood. Such a uniform aggregation scheme easily leads to suboptimal user and item representations, limiting the model expressiveness to some extent. In this work, we address this problem by building bilateral interactive guidance between each user-item pair and proposing a new model named IA-GCN (short for InterActive GCN). Specifically, when learning the user representation from its neighborhood, we assign higher attention weights to those neighbors similar to the target item. Correspondingly, when learning the item representation, we pay more attention to those neighbors resembling the target user. This leads to interactive and interpretable features, effectively distilling target-specific information through each graph convolutional operation. Our model is built on top of LightGCN, a state-of-the-art GCN model for CF, and can be combined with various GCN-based CF architectures in an end-to-end fashion. Extensive experiments on three benchmark datasets demonstrate the effectiveness and robustness of IA-GCN.

Yinan Zhang, Parikshit Sondhi, Anjan Goswami et al.

Faceted browsing is a commonly supported feature of user interfaces for access to information. Existing interfaces generally treat facet values selected by a user as hard filters and respond to the user by only displaying information items strictly satisfying the filters and in their original ranking order. We propose a novel alternative strategy for faceted browsing, called soft faceted browsing, where the system also includes some possibly relevant items outside the selected filter in a non-intrusive way and re-ranks the items to better satisfy the user's information need. Such a soft faceted browsing strategy can be beneficial when the user does not have a very confident and strict preference for the selected facet values, and is especially appropriate for applications such as e-commerce search where the user would like to explore a larger space before finalizing a purchasing decision. We propose a probabilistic framework for modeling and solving the soft faceted browsing problem, and apply the framework to study the case of facet filter selection in e-commerce search engines. Preliminary experiment results demonstrate the soft faceted browsing scheme is better than the traditional faceted browsing scheme in terms of its efficiency in helping users navigate in the information space.

Ching Tarn, Yinan Zhang, Ye Feng

We propose an efficient linear-time graph-based divisive cluster analysis approach called Reductive Clustering. The approach tries to reveal the hierarchical structural information through reducing the graph into a more concise one repeatedly. With the reductions, the original graph can be divided into subgraphs recursively, and a lite informative dendrogram is constructed based on the divisions. The reduction consists of three steps: selection, connection, and partition. First a subset of vertices of the graph are selected as representatives to build a concise graph. The representatives are re-connected to maintain a consistent structure with the previous graph. If possible, the concise graph is divided into subgraphs, and each subgraph is further reduced recursively until the termination condition is met. We discuss the approach, along with several selection and connection methods, in detail both theoretically and experimentally in this paper. Our implementations run in linear time and achieve outstanding performance on various types of datasets. Experimental results show that they outperform state-of-the-art clustering algorithms with significantly less computing resource requirements.