Delving into E-Commerce Product Retrieval with Vision-Language Pre-training

E-commerce search engines comprise a retrieval phase and a ranking phase, where the first one returns a candidate product set given user queries. Recently, vision-language pre-training, combining textual information with visual clues, has been popular in the application of retrieval tasks. In this paper, we propose a novel V+L pre-training method to solve the retrieval problem in Taobao Search. We design a visual pre-training task based on contrastive learning, outperforming common regression-based visual pre-training tasks. In addition, we adopt two negative sampling schemes, tailored for the large-scale retrieval task. Besides, we introduce the details of the online deployment of our proposed method in real-world situations. Extensive offline/online experiments demonstrate the superior performance of our method on the retrieval task. Our proposed method is employed as one retrieval channel of Taobao Search and serves hundreds of millions of users in real time.Comment: 5 pages, 4 figures, accepted to SIRIP 202

ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation

Recommender system (RS) devotes to predicting user preference to a given item and has been widely deployed in most web-scale applications. Recently, knowledge graph (KG) attracts much attention in RS due to its abundant connective information. Existing methods either explore independent meta-paths for user-item pairs over KG, or employ graph neural network (GNN) on whole KG to produce representations for users and items separately. Despite effectiveness, the former type of methods fails to fully capture structural information implied in KG, while the latter ignores the mutual effect between target user and item during the embedding propagation. In this work, we propose a new framework named Adaptive Target-Behavior Relational Graph network (ATBRG for short) to effectively capture structural relations of target user-item pairs over KG. Specifically, to associate the given target item with user behaviors over KG, we propose the graph connect and graph prune techniques to construct adaptive target-behavior relational graph. To fully distill structural information from the sub-graph connected by rich relations in an end-to-end fashion, we elaborate on the model design of ATBRG, equipped with relation-aware extractor layer and representation activation layer. We perform extensive experiments on both industrial and benchmark datasets. Empirical results show that ATBRG consistently and significantly outperforms state-of-the-art methods. Moreover, ATBRG has also achieved a performance improvement of 5.1% on CTR metric after successful deployment in one popular recommendation scenario of Taobao APP.Comment: Accepted by SIGIR 2020, full paper with 10 pages and 5 figure

MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction

Click-through rate (CTR) prediction is a critical task for many industrial systems, such as display advertising and recommender systems. Recently, modeling user behavior sequences attracts much attention and shows great improvements in the CTR field. Existing works mainly exploit attention mechanism based on embedding product when considering relations between user behaviors and target item. However, this methodology lacks of concrete semantics and overlooks the underlying reasons driving a user to click on a target item. In this paper, we propose a new framework named Multiplex Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex relations between user behaviors and target item to enhance CTR prediction. Multiplex relations consist of meaningful semantics, which can bring a better understanding on users' interests from different perspectives. To explore and model multiplex relations, we propose to incorporate various graphs (e.g., knowledge graph and item-item similarity graph) to construct multiple relational paths between user behaviors and target item. Then Bi-LSTM is applied to encode each path in the path extractor layer. A path fusion network and a path activation network are devised to adaptively aggregate and finally learn the representation of all paths for CTR prediction. Extensive offline and online experiments clearly verify the effectiveness of our framework.Comment: Accepted by CIKM202

Organoids: The current status and biomedical applications

Abstract Organoids are three‐dimensional (3D) miniaturized versions of organs or tissues that are derived from cells with stem potential and can self‐organize and differentiate into 3D cell masses, recapitulating the morphology and functions of their in vivo counterparts. Organoid culture is an emerging 3D culture technology, and organoids derived from various organs and tissues, such as the brain, lung, heart, liver, and kidney, have been generated. Compared with traditional bidimensional culture, organoid culture systems have the unique advantage of conserving parental gene expression and mutation characteristics, as well as long‐term maintenance of the function and biological characteristics of the parental cells in vitro. All these features of organoids open up new opportunities for drug discovery, large‐scale drug screening, and precision medicine. Another major application of organoids is disease modeling, and especially various hereditary diseases that are difficult to model in vitro have been modeled with organoids by combining genome editing technologies. Herein, we introduce the development and current advances in the organoid technology field. We focus on the applications of organoids in basic biology and clinical research, and also highlight their limitations and future perspectives. We hope that this review can provide a valuable reference for the developments and applications of organoids