Mainstream multimodal recommender systems are designed to learn user interest by analyzing user-item interaction graphs. However, what they learn about user interest needs to be completed because historical interactions only record items that best match user interest (i.e., the first-order interest), while suboptimal items are absent. To fully exploit user interest, we propose a Second-Order Interest Learning (SOIL) framework to retrieve second-order interest from unrecorded suboptimal items. In this framework, we build a user-item interaction graph augmented by second-order interest, an interest-aware item-item graph for the visual modality, and a similar graph for the textual modality. In our work, all three graphs are constructed from user-item interaction records and multimodal feature similarity. Similarly to other graph-based approaches, we apply graph convolutional networks to each of the three graphs to learn representations of users and items. To improve the exploitation of both first-order and second-order interest, we optimize the model by implementing contrastive learning modules for user and item representations at both the user-item and item-item levels. The proposed framework is evaluated on three real-world public datasets in online shopping scenarios. Experimental results verify that our method is able to significantly improve prediction performance. For instance, our method outperforms the previous state-of-the-art method MGCN by an average of 8.1% in terms of Recall@10. Code: https://github.com/TL-UESTC/SOIL.

In online advertising, the sample selection bias problem is a major cause of inaccurate conversion rate estimates. Current mainstream solutions only perform causality-based optimization in the click space since the conversion labels in the non-click space are absent. However, optimization for unclicked samples is equally essential because the non-click space contains more samples and user characteristics than the click space. To exploit the unclicked samples, we propose a Direct Dual Propensity Optimization (DDPO) framework to optimize the model directly in impression space with both clicked and unclicked samples. In this framework, we specifically design a click propensity network and a conversion propensity network. The click propensity network is dedicated to ensuring that optimization in the click space is unbiased. The conversion propensity network is designed to generate pseudo-conversion labels for unclicked samples, thus overcoming the challenge of absent labels in non-click space. With these two propensity networks, we are able to perform causality-based optimization in both click space and non-click space. In addition, to strengthen the causal relationship, we design two causal transfer modules for the conversion rate prediction model with the attention mechanism. The proposed framework is evaluated on five real-world public datasets and one private Tencent advertising dataset. Experimental results verify that our method is able to improve the prediction performance significantly. For instance, our method outperforms the previous state-of-the-art method by 7.0% in terms of the Area Under the Curve on the Ali-CCP dataset.

In real-world industrial scenarios, post-click conversion rate (CVR) prediction models are trained offline based on click events and subsequently applied online to both clicked and unclicked events. Unfortunately, unclicked events are inevitably difficult to estimate due to user self-selection, which leads to a degradation of CVR prediction accuracy. In order to estimate the prediction of unclicked events, the current mainstream Doubly Robust (DR) estimators introduce the concept of imputed errors. However, inaccuracies in imputed errors can increase the uncertainty in the generalization bound of CVR predictions, consequently resulting in a decline in the CVR prediction accuracy. To challenge this issue, we first present a theoretical analysis of the bias and variance inherent in DR estimators and then introduce a novel causal estimator that seeks to strike a balance between bias and variance within the DR framework, thus optimizing the learning of the imputation model in a more robust manner. Additionally, drawing inspiration from adversarial learning techniques, we propose a novel dual adversarial component, which learns from both the space level and the task level to eliminate the causal influence of input features on the CTR task (i.e., the click propensity), with the goal of achieving unbiased estimations. Our extensive experimental evaluations, conducted on both the widely used benchmark and the real-world large-scale Internet giant platform, convincingly demonstrate the effectiveness of our proposed scheme. Besides, we have released a high-quality industrial dataset named Tenc-UnionAds used for selection bias research in the advertising field.

Data scarcity is a perpetual challenge of recommendation systems, and researchers have proposed a variety of cross-domain recommendation methods to alleviate the problem of data scarcity in target domains. However, in many real-world cross-domain recommendation systems, the source domain and the target domain are sampled from different data distributions, which obstructs the cross-domain knowledge transfer. In this article, we propose to specifically align the data distributions between the source domain and the target domain to alleviate imbalanced sample distribution and thus challenge the data scarcity issue in the target domain. Technically, our proposed approach builds a dual adversarial adaptation (DAA) framework to adversarially train the target model together with a pre-trained source model. Two domain discriminators play the two-player minmax game with the target model and guide the target model to learn reliable domain-invariant features that can be transferred across domains. At the same time, the target model is calibrated to learn domain-specific information of the target domain. In addition, we formulate our approach as a plug-and-play module to boost existing recommendation systems. We apply the proposed method to address the issues of insufficient data and imbalanced sample distribution in real-world Click-through Rate/Conversion Rate predictions on two large-scale industrial datasets. We evaluate the proposed method in scenarios with and without overlapping users/items, and extensive experiments verify that the proposed method is able to significantly improve the prediction performance on the target domain. For instance, our method can boost PLE with a performance improvement of 15.4% in terms of Area Under Curve compared with single-domain PLE on our private game dataset. In addition, our method is able to surpass single-domain MMoE by 6.85% on the public datasets. Code: https://github.com/TL-UESTC/DAA .

Accurate estimation of customer lifetime value (LTV), which reflects the potential consumption of a user over a period of time, is crucial for the revenue management of online advertising platforms. However, predicting LTV in real-world applications is not an easy task since the user consumption data is usually insufficient within a specific domain. To tackle this problem, we propose a novel cross-domain adaptative framework (CDAF) to leverage consumption data from different domains. The proposed method is able to simultaneously mitigate the data scarce problem and the distribution gap problem caused by data from different domains. To be specific, our method firstly learns a LTV prediction model from a different but related platform with sufficient data provision. Subsequently, we exploit domain-invariant information to mitigate data scarce problem by minimizing the Wasserstein discrepancy between the encoded user representations of two domains. In addition, we design a dual-predictor schema which not only enhances domain-invariant information in the semantic space but also preserves domain-specific information for accurate target prediction. The proposed framework is evaluated on five datasets collected from real historical data on the advertising platform of Tencent Games. Experimental results verify that the proposed framework is able to significantly improve the LTV prediction performance on this platform. For instance, our method can boost DCNv2 with the improvement of 13.7% in terms of AUC on dataset G2. Code: https://github.com/TL-UESTC/CDAF.

An ideal multi-modal recommendation system is supposed to be timely updated with the latest modality information and interaction data because the distribution discrepancy between new data and historical data will lead to severe recommendation performance deterioration. However, upgrading a recommendation system with numerous new data consumes much time and computing resources. To mitigate this problem, we propose a Task-Adversarial Adaptation (TAA) framework, which is able to align data distributions and reduce resource consumption at the same time. This framework is specifically designed to align distributions of embedded features for different recommendation tasks between the source domain (i.e., historical data) and the target domain (i.e., new data). Technically, we design a domain feature discriminator for each task to distinguish which domain a feature comes from. By the two-player min-max game between the feature discriminator and the feature embedding network, the feature embedding network is able to align the source and target data distributions. With the ability to align source and target distributions, we are able to reduce the number of training samples by random sampling. In addition, we formulate the proposed approach as a plug-and-play module to accelerate the model training and improve the performance of mainstream multi-modal multi-task recommendation systems. We evaluate our method by predicting the Click-Through Rate (CTR) in e-commerce scenarios. Extensive experiments verify that our method is able to significantly improve prediction performance and accelerate model training on the target domain. For instance, our method is able to surpass the previous state-of-the-art method by 2.45% in terms of Area Under Curve (AUC) on AliExpress_US dataset while only utilizing one percent of the target data in training. Code: https://github.com/TL-UESTC/TAA.

Generalized zero-shot learning (GZSL) aims to recognize both seen and unseen samples by leveraging the connections between semantic and visual representations. Recently, a majority of GZSL methods focus on generating visual features for unseen categories conditioned on category-level semantic attributes. However, there is a considerable gap between generated features and real unseen features since the generator is trained with only seen samples. The final classifier may get confused by the unfaithful generated features and make misclassification. To alleviate this issue, we propose a dual-aligned feature confusion alleviation (DFCA) framework that simultaneously generates faithful and discriminative features for unseen categories. Specifically, our DFCA attains the faithfulness via a conditional invertible neural network (cINN) and aligns the generated visual features and reconstructed semantic conditions with their real counterparts, respectively. To further encourage distinguishable synthetic features, we learn discriminative category-level semantic conditions for cINN with an attributes mapping layer. To verify the proposed method, we conduct extensive experiments on five widely used benchmarks. Experimental results show that our method outperforms previous state-of-the-arts and successfully alleviates features confusion problem in GZSL. For instance, our method achieves the best performance in terms of seen accuracy, unseen accuracy and harmonic mean accuracy on FLO.

Cross-domain recommendation aims to exploit heterogeneous information from a data-sufficient domain (source domain) to transfer knowledge to a data-scarce domain (target domain). A majority of existing methods focus on unidirectional transfer that leverages the domain-shared information to facilitate the recommendation of the target domain. Nevertheless, it is more beneficial to improve the recommendation performance of both domains simultaneously via a dual transfer learning schema, which is known as bidirectional cross-domain recommendation (BCDR). Existing BCDR methods have their limitations, since they only perform bidirectional transfer learning based on domain-shared representations while neglecting rich information that is private to each domain. In this article, we argue that users may have domain-biased preferences due to the characteristics of that domain. Namely, the domain-specific preference information also plays a critical role in the recommendation. To effectively leverage the domain-specific information, we propose a M ulti-level A ttention-based D omain D isentanglement framework dubbed MADD for BCDR, which explicitly leverages the attention mechanism to construct personalized preference with both domain-invariant and domain-specific features obtained by disentangling raw user embeddings. Specifically, the domain-invariant feature is exploited by domain-adversarial learning while the domain-specific feature is learned by imposing an orthogonal loss. We then conduct a reconstruction process on disentangled features to ensure semantic-sufficiency. After that, we devise a multi-level attention mechanism for these disentangled features, which determines their contributions to the final personalized user preference embedding by dynamically learning the attention scores of individual features. We train the model in a multi-task learning fashion to benefit both domains. Extensive experiments on real-world datasets demonstrate that our model significantly outperforms state-of-the-art cross-domain recommendation approaches.

Data scarcity, e.g., labeled data being either unavailable or too expensive, is a perpetual challenge of recommendation systems. Cross-domain recommendation leverages the label information in the source domain to facilitate the task in the target domain. However, in many real-world cross-domain recommendation systems, the source domain and the target domain are sampled from different data distributions, which obstructs the cross-domain knowledge transfer. In this paper, we propose to specifically align the data distributions between the source domain and the target domain to alleviate imbalanced sample distribution and thus challenge the data scarcity issue in the target domain. Technically, our proposed approach builds an adversarial adaptation (AA) framework to adversarially train the target model together with a pre-trained source model. A domain discriminator plays the two-player minmax game with the target model and guides the target model to learn domain-invariant features that can be transferred across domains. At the same time, the target model is calibrated to learn domain-specific information of the target domain. With such a formulation, the target model not only learns domain-invariant features for knowledge transfer, but also preserves domain-specific information for target recommendation. We apply the proposed method to address the issues of insufficient data and imbalanced sample distribution in real-world Click-Through Rate (CTR)/Conversion Rate (CVR) predictions on a large-scale dataset. Specifically, we formulate our approach as a plug-and-play module to boost existing recommendation systems. Extensive experiments verify that the proposed method is able to significantly improve the prediction performance on the target domain. For instance, our method can boost PLE with a performance improvement of 13.88% in terms of Area Under Curve (AUC) compared with single-domain PLE.

Generalized zero-shot learning (GZSL) aims to recognize samples whose categories may not have been seen at training. Recognizing unseen classes as seen ones or vice versa often leads to poor performance in GZSL. Therefore, distinguishing seen and unseen domains is naturally an effective yet challenging solution for GZSL. In this paper, we present a novel method which leverages both visual and semantic modalities to distinguish seen and unseen categories. Specifically, our method deploys two variational autoencoders to generate latent representations for visual and semantic modalities in a shared latent space, in which we align latent representations of both modalities by Wasserstein distance and reconstruct two modalities with the representations of each other. In order to learn a clearer boundary between seen and unseen classes, we propose a two-stage training strategy which takes advantage of seen and unseen semantic descriptions and searches a threshold to separate seen and unseen visual samples. At last, a seen expert and an unseen expert are used for final classification. Extensive experiments on five widely used benchmarks verify that the proposed method can significantly improve the results of GZSL. For instance, our method correctly recognizes more than 99% samples when separating domains and improves the final classification accuracy from 72.6% to 82.9% on AWA1.

It is widely acknowledged that the success of deep learning is built upon large-scale training data and tremendous computing power. However, the data and computing power are not always available for many real-world applications. In this paper, we address the machine learning problem where it lacks training data and limits computing power. Specifically, we investigate domain adaptation which is able to transfer knowledge from one labeled source domain to an unlabeled target domain, so that we do not need much training data from the target domain. At the same time, we consider the situation that the running environment is confined, e.g., in edge computing the end device has very limited running resources. Technically, we present the Faster Domain Adaptation (FDA) protocol and further report two paradigms of FDA: early stopping and amid skipping. The former accelerates domain adaptation by multiple early exit points. The latter speeds up the adaptation by wisely skip several amid neural network blocks. Extensive experiments on standard benchmarks verify that our method is able to achieve the comparable and even better accuracy but employ much less computing resources. To the best of our knowledge, there are very few works which investigated accelerating knowledge adaptation in the community. This work is expected to inspire the topic for more discussion.

Unsupervised Domain Adaptation (UDA) aims to generalize the knowledge learned from a well-labeled source domain to an unlabled target domain. Recently, adversarial domain adaptation with two distinct classifiers (biclassifier) has been introduced into UDA which is effective to align distributions between different domains. Previous bi-classifier adversarial learning methods only focus on the similarity between the outputs of two distinct classifiers. However, the similarity of the outputs cannot guarantee the accuracy of target samples, i.e., traget samples may match to wrong categories even if the discrepancy between two classifiers is small. To challenge this issue, in this paper, we propose a cross-domain gradient discrepancy minimization (CGDM) method which explicitly minimizes the discrepancy of gradients generated by source samples and target samples. Specifically, the gradient gives a cue for the semantic information of target samples so it can be used as a good supervision to improve the accuracy of target samples. In order to compute the gradient signal of target smaples, we further obtain target pseudo labels through a clustering-based self-supervised learning. Extensive experiments on three widely used UDA datasets show that our method surpasses many previous state-of-the-arts.