In most practical applications such as recommendation systems, display advertising, and so forth, the collected data often contains missing values and those missing values are generally missing-not-at-random, which deteriorates the prediction performance of models. Some existing estimators and regularizers attempt to achieve unbiased estimation to improve the predictive performance. However, variances and generalization bound of these methods are generally unbounded when the propensity scores tend to zero, compromising their stability and robustness. In this paper, we first theoretically reveal that limitations of regularization techniques. Besides, we further illustrate that, for more general estimators, unbiasedness will inevitably lead to unbounded variance. These general laws inspire us that the estimator designs is not merely about eliminating bias, reducing variance, or simply achieve a bias-variance trade-off. Instead, it involves a quantitative joint optimization of bias and variance. Then, we develop a systematic fine-grained dynamic learning framework to jointly optimize bias and variance, which adaptively selects an appropriate estimator for each user-item pair according to the predefined objective function. With this operation, the generalization bounds and variances of models are reduced and bounded with theoretical guarantees. Extensive experiments are conducted to verify the theoretical results and the effectiveness of the proposed dynamic learning framework.

Cross-Domain Sequential Recommendation (CDSR) methods aim to tackle the data sparsity and cold-start problems present in Single-Domain Sequential Recommendation (SDSR). Existing CDSR works design their elaborate structures relying on overlapping users to propagate the cross-domain information. However, current CDSR methods make closed-world assumptions, assuming fully overlapping users across multiple domains and that the data distribution remains unchanged from the training environment to the test environment. As a result, these methods typically result in lower performance on online real-world platforms due to the data distribution shifts. To address these challenges under open-world assumptions, we design an Adaptive Multi-Interest Debiasing framework for cross-domain sequential recommendation (AMID), which consists of a multi-interest information module (MIM) and a doubly robust estimator (DRE). Our framework is adaptive for open-world environments and can improve the model of most off-the-shelf single-domain sequential backbone models for CDSR. Our MIM establishes interest groups that consider both overlapping and non-overlapping users, allowing us to effectively explore user intent and explicit interest. To alleviate biases across multiple domains, we developed the DRE for the CDSR methods. We also provide a theoretical analysis that demonstrates the superiority of our proposed estimator in terms of bias and tail bound, compared to the IPS estimator used in previous work. To promote related research in the community under open-world assumptions, we collected an industry financial CDSR dataset from Alipay, called "MYbank-CDR". Extensive offline experiments on four industry CDSR scenarios including the Amazon and MYbank-CDR datasets demonstrate the remarkable performance of our proposed approach. Additionally, we conducted a standard A/B test on Alipay, a large-scale financial platform with over one billion users, to validate the effectiveness of our model under open-world assumptions. Code and dataset are available at https://github.com/WujiangXu/AMID.

Video summarization is an essential problem in signal processing, which intends to produce a concise summary of the original video. Existing video summarization approaches regard the task as a keyframe selection problem and generally construct the frame-wise representation by combining the long-range temporal dependency with either unimodal or bimodal information. The optimal keyframe should offer the semantic summarization of the whole content by exploiting the multimodal and shot-level hierarchical natures of videos, however, such natures are not fully exploited in existing methods. In this paper, we propose to construct a more powerful and robust frame-wise representation and predict the frame-level importance score in a fair and comprehensive manner. Specifically, we propose a multimodal hierarchical shot-aware convolutional network, denoted as MHSCNet, to enhance the frame-wise representation via combining the comprehensive available multimodal information. We further design a hierarchical ShotConv network to incorporate the adaptive shot-aware frame-level representation by considering the short-range and long-range temporal dependencies. Based on the learned shot-aware representations, MHSCNet can predict the frame-level importance score in the local and global view of the video. Extensive experiments on two standard video summarization datasets demonstrate that our proposed method consistently outperforms state-of-the-arts.

Multi-Target Cross Domain Recommendation(CDR) has attracted a surge of interest recently, which intends to improve the recommendation performance in multiple domains (or systems) simultaneously. Most existing multi-target CDR frameworks primarily rely on the existence of the majority of overlapped users across domains. However, general practical CDR scenarios cannot meet the strictly overlapping requirements and only share a small margin of common users across domains. Additionally, the majority of users have quite a few historical behaviors in such small-overlapping CDR scenarios. To tackle the aforementioned issues, we propose a simple-yet-effective neural node matching based framework for more general CDR settings, i.e., only (few) partially overlapped users exist across domains and most overlapped as well as non-overlapped users do have sparse interactions. The present framework mainly contains two modules: (i) intra-to-inter node matching module, and (ii) intra node complementing module. Concretely, the first module conducts intra-knowledge fusion within each domain and subsequent inter-knowledge fusion across domains by fully connected user-user homogeneous graph information aggregating. By doing this, the knowledge of all users, especially the non-overlapping users, could be well extracted and transferred without relying heavily on overlapping users. The second module introduces user-item matching to complement the potential missing interactions for each user and correct his/her under-represented representations, especially for the users with observed sparse interactions. Essentially, companion objectives are also inserted into each module to guide the knowledge transferring procedures, which leads to positive effects on multiple domains simultaneously. Extensive experiments on four multi-target CDR tasks from both public and real-world large-scale financial industry datasets demonstrate the remarkable performance of our proposed approach. Our code is publicly available at the link: https://github.com/WujiangXu/NMCDRR.

In online recommendation, financial service, etc., the most common application of multi-task learning (MTL) is the multi-step conversion estimations. A core property of the multi-step conversion is the sequential dependence among tasks. However, most existing works focus far more on the specific post-view click-through rate (CTR) and post-click conversion rate (CVR) estimations, which neglect the generalization of sequential dependence multi-task learning (SDMTL). Additionally, the performance of the SDMTL framework is also deteriorated by the interference derived from implicitly conflict information passing between adjacent tasks. In this paper, a systematic learning paradigm of the SDMTL problem is established for the first time, which can transform the SDMTL problem into a general MTL problem with constraints and be applicable to more general multi-step conversion scenarios with stronger task dependence. Also, the distribution dependence relationship between adjacent task spaces is illustrated from a theoretical point of view. On the other hand, an SDMTL architecture, named Task Aware Feature Extraction (TAFE), is developed to enable dynamic task representation learning from a sample-wise view. TAFE selectively reconstructs the implicit shared information corresponding to each sample case and performs explicit task-specific extraction under dependence constraints. Extensive experiments on offline public and real-world industrial datasets, and online A/B implementations demonstrate the effectiveness and applicability of proposed theoretical and implementation frameworks.

Data sparsity has been a long-standing issue for accurate and trustworthy recommendation systems (RS). To alleviate the problem, many researchers pay much attention to cross-domain recommendation (CDR), which aims at transferring rich knowledge from related source domains to enhance the recommendation performance of sparse target domain. To reach the knowledge transferring purpose, recent CDR works always focus on designing different pairwise directed or undirected information transferring strategies between source and target domains. However, such pairwise transferring idea is difficult to adapt to multi-target CDR scenarios directly, e.g., transferring knowledge between multiple domains and improving their performance simultaneously, as such strategies may lead the following issues: (1) When the number of domains increases, the number of transferring modules will grow exponentially, which causes heavy computation complexity. (2) A single pairwise transferring module could only capture the relevant information of two domains, but ignores the correlated information of other domains, which may limit the transferring effectiveness. (3) When a sparse domain serves as the source domain during the pairwise transferring, it would easily leads the negative transfer problem, and the untrustworthy information may hurt the target domain recommendation performance. In this article, we consider the key challenge of the multi-target CDR task: How to identify the most valuable trustworthy information over multiple domains and transfer such information efficiently to avoid the negative transfer problem? To fulfill the above challenge, we propose a novel end-to-end model termed as DR-MTCDR , standing for D isentangled R epresentations learning for M ulti- T arget CDR . DR-MTCDR aims at transferring the trustworthy domain-shared information across domains, which has the two major advantages in both efficiency and effectiveness: (1) For efficiency, DR-MTCDR utilizes a unified module on all domains to capture disentangled domain-shared information and domain-specific information, which could support all domain recommendation and be insensitive to the number of domains. (2) For effectiveness, based on the disentangled domain-shared and domain-specific information, DR-MTCDR has the capability to lead positive effect and make trustworthy recommendation for each domain. Empirical evaluations on datasets from both public datasets and real-world large-scale financial datasets have shown that the proposed framework outperforms other state-of-the-art baselines.

Sequential recommendation (SR) learns users’ preferences by capturing the sequential patterns from users’ behaviors evolution. As discussed in many works, user–item interactions of SR generally present the intrinsic power-law distribution, which can be ascended to hierarchy-like structures. Previous methods usually handle such hierarchical information by making user–item sectionalization empirically under Euclidean space, which may cause distortion of user–item representation in real online scenarios. In this article, we propose a Poincaré-based heterogeneous graph neural network named Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation (PHGR) to model the sequential pattern information as well as hierarchical information contained in the data of SR scenarios simultaneously. Specifically, for the purpose of explicitly capturing the hierarchical information, we first construct a weighted user–item heterogeneous graph by aliening all the user–item interactions to improve the perception domain of each user from a global view. Then the output of the global representation would be used to complement the local directed item–item homogeneous graph convolution. By defining a novel hyperbolic inner product operator, the global and local graph representation learning are directly conducted in Poincaré ball instead of commonly used projection operation between Poincaré ball and Euclidean space, which could alleviate the cumulative error issue of general bidirectional translation process. Moreover, for the purpose of explicitly capturing the sequential dependency information, we design two types of temporal attention operations under Poincaré ball space. Empirical evaluations on datasets from the public and financial industry show that PHGR outperforms several comparison methods.