Automated Recommender Systems

Recommender systems have been existing accompanying by web development, driving personalized experience for billions of users. They play a vital role in the information retrieval process, overcome the information overload by facilitating the communication between business people and the public, and boost the business world. Powered by the advances of machine learning techniques, modern recommender systems enable tremendous automation on the data preprocessing, information distillations, and contextual inferences. It allows us to mine patterns and relationships from massive datasets and various data resources to make inferences. Moreover, the fast evolvement of deep learning techniques brings vast vitality and improvements dived in both academic research and industry applications. Despite the prominence achieved in the recent recommender systems, the automation they have been achieved is still limited in a narrow scope. On the one hand, beyond the static setting, real-world recommendation tasks are often imbued with high-velocity streaming data. On the other hand, with the increasing complexity of model structure and system architecture, the handcrafted design and tuning process is becoming increasingly complicated and time-consuming. With these challenges in mind, this dissertation aims to enable advanced automation in recommender systems. In particular, we discuss how to update factorization-based recommendation models adaptively and how to automatically design and tune recommendation models with automated machine learning techniques. Four main contributions are made via tackling the challenges: (1) The first contribution of this research dissertation is the development of a tensor-based algorithm for streaming recommendation tasks. (2) As deep learning techniques have shown their superiority in recommendation tasks and become dominant in both academia and industry applications, the second contribution is exploring and developing advanced deep learning algorithms to tackle the recommendation problem with the streaming dataset. (3) To alleviate the burden of human efforts, we explore adopting automated machine learning in designing and tuning recommender systems. The third contribution of this dissertation is the development of a novel neural architecture search approaches for discovering useful features interactions and designing better models for the click-through rate prediction problem. (4) Considering a large number of recommendation tasks in industrial applications and their similarities, in the last piece of work work, we focus on the hyperparameter tuning problem in the transfer-learning setting and develop a transferable framework for meta-level tuning of machine learning models

Integrated DNA methylation, transcriptome and physiological analyses reveal new insights into superiority of poplars formed by interspecific grafting

Plant grafting has a long history and it is extensively employed to improve plant performance. In our previous research, reciprocal grafts of Populus cathayana Rehder (C) and Populus deltoides Bart. Ex Marsh (D) were generated. The results showed that interspecific grafting combinations (scion/rootstock: C/D and D/C) grew better than intraspecific grafting combinations (C/C and D/D). To further understand differences in molecular mechanisms between interspecific and intraspecific grafting, we performed an integrated analysis, including bisulfite sequencing, RNA sequencing and measurements of physiological indicators, to investigate leaves of different grafting combinations. We found that the difference at the genome-wide methylation level was greater in D/C vs D/D than in C/D vs C/C, but no difference was detected at the transcription level in D/C vs D/D. Furthermore, the grafting superiority of D/C vs D/D was not as strong as that of C/D vs C/C. These results may be associated with the different methylation forms, mCHH (71.76%) and mCG (57.16%), that accounted for the highest percentages in C/D vs C/C and D/C vs D/D, respectively. In addition, the interspecific grafting superiority was found mainly related to the process of photosynthesis, phytohormone signal transduction, biosynthesis of secondary metabolites, cell wall and transcriptional regulation based on both physiological and molecular results. Overall, the results indicated that the physiological and molecular phenotypes of grafted plants are affected by the interaction between scion and rootstock. Thus, our study provides a theoretical basis for developing suitable scion-rootstock combinations for grafted plants.Peer reviewe