The personalized bundle generation problem, which aims to create a preferred bundle for user from numerous candidate items, receives increasing attention in recommendation. However, existing works ignore the order-invariant nature of the bundle and adopt sequential modeling methods as the solution, which might introduce inductive bias and cause a large latency in prediction. To address this problem, we propose to perform the bundle generation via non-autoregressive mechanism and design a novel encoder-decoder framework named BundleNAT, which can effectively output the targeted bundle in one-shot without relying on any inherent order. In detail, instead of learning sequential dependency, we propose to adopt pre-training techniques and graph neural network to fully embed user-based preference and item-based compatibility information, and use a self-attention based encoder to further extract global dependency pattern. We then design a permutation-equivariant decoding architecture that is able to directly output the desired bundle in a one-shot manner. Experiments on three real-world datasets from Youshu and Netease show the proposed BundleNAT significantly outperforms the current state-of-the-art methods in average by up to 35.92%, 10.97% and 23.67% absolute improvements in Precision, Precision+, and Recall, respectively.

Heterogeneous information network (HIN), which contains rich semantics depicted by meta-paths, has become a powerful tool to alleviate data sparsity in recommender systems. Existing HIN-based recommendations hold the data centralized storage assumption and conduct centralized model training. However, the real-world data is often stored in a distributed manner for privacy concerns, resulting in the failure of centralized HIN-based recommendations. In this paper, we suggest the HIN is partitioned into private HINs stored in the client side and shared HINs in the server. Following this setting, we propose a federated heterogeneous graph neural network (FedHGNN) based framework, which can collaboratively train a recommendation model on distributed HINs without leaking user privacy. Specifically, we first formalize the privacy definition in the light of differential privacy for HIN-based federated recommendation, which aims to protect user-item interactions of private HIN as well as user's high-order patterns from shared HINs. To recover the broken meta-path based semantics caused by distributed data storage and satisfy the proposed privacy, we elaborately design a semantic-preserving user interactions publishing method, which locally perturbs user's high-order patterns as well as related user-item interactions for publishing. After that, we propose a HGNN model for recommendation, which conducts node- and semantic-level aggregations to capture recovered semantics. Extensive experiments on three datasets demonstrate our model outperforms existing methods by a large margin (up to 34% in HR@10 and 42% in NDCG@10) under an acceptable privacy budget.