In real-world recommender systems, user engagement and subjective feedback play pivotal roles in shaping the content distribution mechanism of the platform. When platforms reach a certain scale, they often gather valuable questionnaire feedback data from users to evaluate their satisfaction with recommended items. Compared to traditional user feedback such as likes, questionnaires explicitly capture both satisfaction and dissatisfaction and are unaffected by other users' questionnaires, thus better expressing users' true preferences. In this paper, we aim to leverage the questionnaire feedback to align the recommendation model with users' true preferences. However, due to the platform distribution mechanism and divergent user attitudes toward questionnaires, the questionnaire feedback data frequently becomes sparse and exhibits selection biases, resulting in challenges in feature integration and training process. To address these issues, we introduce a novel user Satisfaction Alignment framework that effectively leverages Questionnaire feedback to enhance Recommendation, named SAQRec. SAQRec begins by training an unbiased satisfaction model to impute satisfaction, addressing selection bias and data sparsity. Then, SAQRec aligns features with users' true preferences by disentangling satisfaction and dissatisfaction from click history and categorizing clicked items into multiple satisfaction levels through the imputed satisfactions. Additionally, the imputed satisfactions from the pre-trained unbiased satisfaction model serve as pseudo-labels to align the model's outputs with users' true preferences. Extensive experiments on both public and commercial datasets demonstrate SAQRec's superior integration of questionnaire feedback in recommendation models. Online A/B testing on a short video platform confirms its effectiveness in boosting user watch time and positive-to-negative feedback ratio, enhancing overall performance and user satisfaction.

Modern personalized recommendation services often rely on user feedback, either explicit or implicit, to improve the quality of services. Explicit feedback refers to behaviors like ratings, while implicit feedback refers to behaviors like user clicks. However, in the scenario of full-screen video viewing experiences like Tiktok and Reels, the click action is absent, resulting in unclear feedback from users, hence introducing noises in modeling training. Existing approaches on de-noising recommendation mainly focus on positive instances while ignoring the noise in a large amount of sampled negative feedback. In this paper, we propose a meta-learning method to annotate the unlabeled data from loss and gradient perspectives, which considers the noises in both positive and negative instances. Specifically, we first propose anInverse Dual Loss (IDL) to boost the true label learning and prevent the false label learning. Then we further propose anInverse Gradient (IG) method to explore the correct updating gradient and adjust the updating based on meta-learning. Finally, we conduct extensive experiments on both benchmark and industrial datasets where our proposed method can significantly improve AUC by 9.25% against state-of-the-art methods. Further analysis verifies the proposed inverse learning framework is model-agnostic and can improve a variety of recommendation backbones. The source code, along with the best hyper-parameter settings, is available at this link: https://github.com/Guanyu-Lin/InverseLearning.

Recommender system is one of the most important information services on today’s Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommender systems. In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems. We first introduce the background and the history of the development of both recommender systems and graph neural networks. For recommender systems, in general, there are four aspects for categorizing existing works: stage, scenario, objective, and application. For graph neural networks, the existing methods consist of two categories: spectral models and spatial ones. We then discuss the motivation of applying graph neural networks into recommender systems, mainly consisting of the high-order connectivity, the structural property of data and the enhanced supervision signal. We then systematically analyze the challenges in graph construction, embedding propagation/aggregation, model optimization, and computation efficiency. Afterward and primarily, we provide a comprehensive overview of a multitude of existing works of graph neural network-based recommender systems, following the taxonomy above. Finally, we raise discussions on the open problems and promising future directions in this area. We summarize the representative papers along with their code repositories in https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems .

Session-based recommendation (SBR) aims to recommend items based on user behaviors in a session. For the online life service platforms, such as Meituan, both the user's location and the current time primarily cause the different patterns and intents in user behaviors. Hence, spatiotemporal context plays a significant role in the recommendation on those platforms, which motivates an important problem of spatiotemporal-aware session-based recommendation (STSBR). Since the spatiotemporal context is introduced, there are two critical challenges: 1) how to capture session-level relations of spatiotemporal context (inter-session view), and 2) how to model the complex user decision-making process at a specific location and time (intra-session view). To address them, we propose a novel solution named STAGE in this paper. Specifically, STAGE first constructs a global information graph to model the multi-level relations among all sessions, and a session decision graph to capture the complex user decision process for each session. STAGE then performs inter-session and intra-session embedding propagation on the constructed graphs with the proposed graph attentive convolution (GAC) to learn representations from the above two perspectives. Finally, the learned representations are combined with spatiotemporal-aware soft-attention for final recommendation. Extensive experiments on two datasets from Meituan demonstrate the superiority of STAGE over state-of-the-art methods. Further studies also verify that each component is effective.

Consumption intent, defined as the decision-driven force of consumption behaviors, is crucial for improving the explainability and performance of user-modeling systems, with various downstream applications like recommendation and targeted marketing. However, consumption intent is implicit, and only a few known intents have been explored from the user consumption data in Meituan. Hence, discovering new consumption intents is a crucial but challenging task, which suffers from two critical challenges: 1) how to encode the consumption intent related to multiple aspects of preferences, and 2) how to discover the new intents with only a few known ones. In Meituan, we designed the AutoIntent system, consisting of the disentangled intent encoder and intent discovery decoder, to address the above challenges. Specifically, for the disentangled intent encoder, we construct three groups of dual hypergraphs to capture the high-order relations under the three aspects of preferences and then utilize the designed hypergraph neural networks to extract disentangled intent features. For the intent discovery decoder, we propose to build intent-pair pseudo labels based on the denoised feature similarities to transfer knowledge from known intents to new ones. Extensive offline evaluations verify that AutoIntent can effectively discover unknown consumption intents. Moreover, we deploy AutoIntent in the recommendation engine of the Meituan APP, and the further online evaluation verifies its effectiveness.

Session-based recommendation (SBR) aims at the next-item prediction with a short behavior session. Existing solutions fail to address two main challenges: 1) user interests are shown as dynamically coupled intents, and 2) sessions always contain noisy signals. To address them, in this paper, we propose a hypergraph-based solution, HIDE. Specifically, HIDE first constructs a hypergraph for each session to model the possible interest transitions from distinct perspectives. HIDE then disentangles the intents under each item click in micro and macro manners. In the micro-disentanglement, we perform intent-aware embedding propagation on session hypergraph to adaptively activate disentangled intents from noisy data. In the macro-disentanglement, we introduce an auxiliary intent-classification task to encourage the independence of different intents. Finally, we generate the intent-specific representations for the given session to make the final recommendation. Benchmark evaluations demonstrate the significant performance gain of our HIDE over the state-of-the-art methods.

Widely applied in today's recommender systems, sequential recommendation predicts the next interacted item for a given user via his/her historical item sequence. However, sequential recommendation suffers data sparsity issue like most recommenders. To extract auxiliary signals from the data, some recent works exploit self-supervised learning to generate augmented data via dropout strategy, which, however, leads to sparser sequential data and obscure signals. In this paper, we propose D ual C ontrastive N etwork (DCN) to boost sequential recommendation, from a new perspective of integrating auxiliary user-sequence for items. Specifically, we propose two kinds of contrastive learning. The first one is the dual representation contrastive learning that minimizes the distances between embeddings and sequence-representations of users/items. The second one is the dual interest contrastive learning which aims to self-supervise the static interest with the dynamic interest of next item prediction via auxiliary training. We also incorporate the auxiliary task of predicting next user for a given item's historical user sequence, which can capture the trends of items preferred by certain types of users. Experiments on benchmark datasets verify the effectiveness of our proposed method. Further ablation study also illustrates the boosting effect of the proposed components upon different sequential models.