Abstract-Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising. The data in those applications is mostly categorical and contains multiple fields; a typical representation is to transform it into a high-dimensional sparse binary feature representation via one-hot encoding. Facing with the extreme sparsity, traditional models may limit their capacity of mining shallow patterns from the data, i.e. low-order feature combinations. Deep models like deep neural networks, on the other hand, cannot be directly applied for the high-dimensional input because of the huge feature space. In this paper, we propose a Product-based Neural Networks (PNN) with an embedding layer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between interfield categories, and further fully connected layers to explore high-order feature interactions. Our experimental results on two large-scale real-world ad click datasets demonstrate that PNNs consistently outperform the state-of-the-art models on various metrics.

Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize specific goals such as maximizing revenue and return on investment (ROI) led by ad placements, advertisers not only need to estimate the relevance between the ads and user's interests, but most importantly require a strategic response with respect to other advertisers bidding in the market. In this paper, we formulate bidding optimization with multi-agent reinforcement learning. To deal with a large number of advertisers, we propose a clustering method and assign each cluster with a strategic bidding agent. A practical Distributed Coordinated Multi-Agent Bidding (DCMAB) has been proposed and implemented to balance the tradeoff between the competition and cooperation among advertisers. The empirical study on our industry-scaled real-world data has demonstrated the effectiveness of our methods. Our results show cluster-based bidding would largely outperform single-agent and bandit approaches, and the coordinated bidding achieves better overall objectives than purely self-interested bidding agents.

Metal‐coordinated supramolecular nanoassemblies have recently attracted extensive attention as materials for cancer theranostics. Owing to their unique physicochemical properties, metal‐coordinated supramolecular self‐assemblies can bridge the boundary between traditional inorganic and organic materials. By tailoring the structural components of the metal ions and binding ligands, numerous multifunctional theranostic nanomedicines can be constructed. Metal‐coordinated supramolecular nanoassemblies can modulate the tumor microenvironment (TME), thus facilitating the development of TME‐responsive nanomedicines. More importantly, TME‐responsive organic–inorganic hybrid nanomaterials can be constructed in vivo by exploiting the metal‐coordinated self‐assembly of a variety of functional ligands, which is a promising strategy for enhancing the tumor accumulation of theranostic molecules. In this review, recent advancements in the design and fabrication of metal‐coordinated supramolecular nanomedicines for cancer theranostics are highlighted. These supramolecular compounds are classified according to the order in which the coordinated metal ions appear in the periodic table. Furthermore, the prospects and challenges of metal‐coordinated supramolecular self‐assemblies for both technical advances and clinical translation are discussed. In particular, the superiority of TME‐responsive nanomedicines for in vivo coordinated self‐assembly is elaborated, with an emphasis on strategies that enhance the accumulation of functional components in tumors for an ideal theranostic outcome.

No abstract

No abstract