Commodity recommendation contributes an important part of individuals’ daily life. In this context, deep reinforcement learning methods have demonstrated substantial efficacy in enhancing recommender systems’ performance. Nevertheless, several recommender systems directly utilize original feature information as a foundational element for decision-making, which seems simplistic and low efficient. Furthermore, the incorporation of sequential decision-making adds complexity to the task of recommendation. In pursuit of maximizing the long-term sequential returns of recommender systems, our study introduces a novel architecture, named User Information Separating Architecture (UISA). This framework is tailored to align with classic reinforcement learning algorithms and aims to extract the user’s interest value through the discrete processing of both static and dynamic user information. Through integration with deep reinforcement learning, the architecture is oriented towards the maximization of long-term profit and is applicable in sequential recommendation scenarios. We conduct experimental assessments by combining the proposed architecture with proximal policy optimization (PPO) and deep deterministic policy gradient (DDPG) algorithms. The outcomes illustrate marked improvements in commodity recommendation, showcasing enhancements ranging from approximately 5% to 40% in both reward and click-through rate metrics across a self-constructed JDEnv environment and the Virtual Taobao environment. Through comparison experiments, the UISA models demonstrate comparable performance.

Widespread adoption of online recruitment platforms has led to explosive growth in employment information, resulting in an ever-increasing demand from job seekers for accurate and effective job recommendations. Existing studies on the Person-Job Fit models focus on the correlation between resumes and job descriptions, with rare consideration given to user historical behavior such as click and application. On the contrary, job recommendation methods always ignore the crucial information lurking in the resume text. In addition, the continuous influx of a vast amount of job data poses challenges to the updating of online recommendation results. To this end, we propose a novel O nline J ob R ecommendation model via R esume F usion (OJRRF) in this article, aimed at making accurate and efficient online job recommendations with the merits of addressing job cold start and long tail problems. The key contribution lies in two facets: (1) incorporating resume text information into the knowledge graph attention framework to enhance job seekers’ vector representations jointly; (2) designing a hybrid recommender strategy by combining the knowledge-aware offline model with the content-based online model. Finally, we conducted extensive comparison experiments and online A/B test on the recruitment platform of JiuYeJie big data company to validate the effectiveness and real-time capability of OJRRF. The release code can be found in https://github.com/urnotada/OJRRF .

In a conventional supervised learning paradigm, each data instance is associated with one single class label. Multilabel learning differs in the way that data instances may belong to multiple concepts simultaneously, which naturally appear in a variety of high impact domains, ranging from bioinformatics and information retrieval to multimedia analysis. It targets leveraging the multiple label information of data instances to build a predictive learning model that can classify unlabeled instances into one or multiple predefined target classes. In multilabel learning, even though each instance is associated with a rich set of class labels, the label information could be noisy and incomplete as the labeling process is both time consuming and labor expensive, leading to potential missing annotations or even erroneous annotations. The existence of noisy and missing labels could negatively affect the performance of underlying learning algorithms. More often than not, multilabeled data often has noisy, irrelevant, and redundant features of high dimensionality. The existence of these uninformative features may also deteriorate the predictive power of the learning model due to the curse of dimensionality. Feature selection, as an effective dimensionality reduction technique, has shown to be powerful in preparing high-dimensional data for numerous data mining and machine-learning tasks. However, a vast majority of existing multilabel feature selection algorithms either boil down to solving multiple single-labeled feature selection problems or directly make use of the imperfect labels to guide the selection of representative features. As a result, they may not be able to obtain discriminative features shared across multiple labels. In this article, to bridge the gap between a rich source of multilabel information and its blemish in practical usage, we propose a novel noise-resilient multilabel informed feature selection framework (MIFS) by exploiting the correlations among different labels. In particular, to reduce the negative effects of imperfect label information in obtaining label correlations, we decompose the multilabel information of data instances into a low-dimensional space and then employ the reduced label representation to guide the feature selection phase via a joint sparse regression framework. Empirical studies on both synthetic and real-world datasets demonstrate the effectiveness and efficiency of the proposed MIFS framework.

Recent years have witnessed the prevalence of networked data in various domains. Among them, a large number of networks are not only topologically structured but also have a rich set of features on nodes. These node features are usually of high dimensionality with noisy, irrelevant and redundant information, which may impede the performance of other learning tasks. Feature selection is useful to alleviate these critical issues. Nonetheless, a vast majority of existing feature selection algorithms are predominantly designed in a static setting. In reality, real-world networks are naturally dynamic, characterized by both topology and content changes. It is desirable to capture these changes to find relevant features tightly hinged with network structure continuously, which is of fundamental importance for many applications such as disaster relief and viral marketing. In this paper, we study a novel problem of time-evolving feature selection for dynamic networks in an unsupervised scenario. Specifically, we propose a TeFS framework by leveraging the temporal evolution property of dynamic networks to update the feature selection results incrementally. Experimental results show the superiority of TeFS over the state-of-the-art batch-mode unsupervised feature selection algorithms.