Conversion rate (CVR) prediction is essential in recommender systems, facilitating precise matching between recommended items and users’ preferences. However, the sample selection bias (SSB) and data sparsity (DS) issues pose challenges to accurate prediction. Existing works have proposed the click-through and conversion rate (CTCVR) prediction task which models samples from exposure to ``click and conversion" in entire space and incorporates multi-task learning. This approach has shown efficacy in mitigating these challenges. Nevertheless, it intensifies the false negative sample (FNS) problem. To be more specific, the CTCVR task implicitly treats all the CVR labels of non-click samples as negative, overlooking the possibility that some samples might convert if clicked. This oversight can negatively impact CVR model performance, as empirical analysis has confirmed. To this end, we advocate for discarding the CTCVR task and proposing a Non-click samples Improved Semi-supErvised (NISE) method for conversion rate prediction, where the non-click samples are treated as unlabeled. Our approach aims to predict their probabilities of conversion if clicked, utilizing these predictions as pseudo-labels for further model training. This strategy can help alleviate the FNS problem, and direct modeling of the CVR task across the entire space also mitigates the SSB and DS challenges. Additionally, we conduct multi-task learning by introducing an auxiliary click-through rate prediction task, thereby enhancing embedding layer representations. Our approach is applicable to various multi-task architectures. Comprehensive experiments are conducted on both public and production datasets, demonstrating the superiority of our proposed method in mitigating the FNS challenge and improving the CVR estimation. The implementation code is available at https://github.com/Hjh233/NISE.

Online convex optimization in distributed systems has shown great promise in collaboratively learning on data streams with massive learners, such as in collaborative coordination in robot and IoT networks. When implemented in communication-constrained networks like robot and IoT networks, two critical yet distinct objectives in distributed online convex optimization (DOCO) are minimizing the overall regret and the communication cost. Achieving both objectives simultaneously is challenging, especially when the number of learners$n$and learning time$T$are prohibitively large. To address this challenge, we propose novel algorithms in typical adversarial and stochastic settings. Our algorithms significantly reduce the communication complexity of the algorithms with the state-of-the-art regret by a factor of$\mathcal {O}(n^{2})$and$\tilde{\mathcal {O}}(\sqrt{nT})$in adversarial and stochastic settings, respectively. We are the first to achieve nearly optimal regret and communication complexity simultaneously up to polylogarithmic factors. We validate our algorithms through experiments on real-world datasets in classification tasks. Our algorithms with appropriate parameters can achieve$90\%\sim 99\%$communication saving with close accuracy over existing methods in most cases. The code is available athttps://github.com/GGBOND121382/Communication-Efficient_Regret-Optimal_DOCO.

The vibrant markets offering data annotation services are fast-growing and play an important part in machine learning. While many multi-label prediction services are available, it is challenging for consumers to decide which services to use for their own tasks and budgets due to the heterogeneity in those services’ labeling categories, labeling quality and price. In this paper, we focus on a practical problem of obtaining high-quality multi-label annotation data from multiple services within a budget constraint. We propose a framework that firstly parameterizes the labeling generation based on the constructed Probabilistic Graph Model, and designs an Expectation Maximization(EM)-based iteration algorithm to estimate the service labeling quality and task truth distribution. Then we transform the annotation service selection strategy into an adaptive submodular maximization coverage problem, which motivates us to design an adaptive random greedy algorithm with a constant approximation ratio 1−1/e. We evaluate our design on both real-world experiments and a series of simulations on various machine learning models and real datasets. These experiments will show that our method has more accuracy and reliability improvements.

Crowdsourcing is a widely adopted way for various labor-intensive tasks. One of the core problems in crowdsourcing systems is how to assign tasks to most suitable workers for better results, which heavily relies on the accurate profiling of each worker's reliability for different topics of tasks. Many previous work have studied worker reliability for either explicit topics represented by task descriptions or latent topics for categorical tasks. In this work, we aim to accurately estimate more fine-grained worker reliability for latent topics in numerical tasks, so as to further improve the result quality. We propose a bayesian probabilistic model named Gaussian Latent Topic Model(GLTM) to mine the latent topics of numerical tasks based on workers' behaviors and to estimate workers' topic-level reliability. By utilizing the GLTM, we propose a truth inference algorithm named TI-GLTM to accurately infer the tasks' truth and topics simultaneously and dynamically update workers' topic-level reliability. We also design an online task assignment mechanism called MRA-GLTM, which assigns appropriate tasks to workers with the Maximum Reduced Ambiguity principle. The experiment results show our algorithms can achieve significantly lower MAE and MSE than that of the state-of-the-art approaches.