Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

Implicit feedback is frequently used for developing personalized recommendation services due to its ubiquity and accessibility in real-world systems. In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user. However, most of these methods treat all the training triplets equally, which ignores the subtle difference between different positive or negative items. On the other hand, even though some other works make use of the auxiliary information (e.g., dwell time) of user behaviors to capture this subtle difference, such auxiliary information is hard to obtain. To mitigate the aforementioned problems, we propose a novel training framework named Triplet Importance Learning (TIL), which adaptively learns the importance score of training triplets. We devise two strategies for the importance score generation and formulate the whole procedure as a bilevel optimization, which does not require any rule-based design. We integrate the proposed training procedure with several Matrix Factorization (MF)- and Graph Neural Network (GNN)-based recommendation models, demonstrating the compatibility of our framework. Via a comparison using three real-world datasets with many state-of-the-art methods, we show that our proposed method outperforms the best existing models by 3-21\% in terms of Recall@k for the top-k recommendation

Knowledge-Enhanced Top-K Recommendation in Poincar\'e Ball

Personalized recommender systems are increasingly important as more content and services become available and users struggle to identify what might interest them. Thanks to the ability for providing rich information, knowledge graphs (KGs) are being incorporated to enhance the recommendation performance and interpretability. To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs. Furthermore, a hyperbolic attention network is employed to determine the relative importances of neighboring entities of a certain item. In addition, we propose an adaptive and fine-grained regularization mechanism to adaptively regularize items and their neighboring representations. Via a comparison using three real-world datasets with state-of-the-art methods, we show that the proposed model outperforms the best existing models by 2-16% in terms of NDCG@K on Top-K recommendation.Comment: Accepted by the 35th AAAI Conference on Artificial Intelligence (AAAI 2021

Result Diversification in Search and Recommendation: A Survey

Diversifying return results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of providers. There has been growing attention on diversity-aware research during recent years, accompanied by a proliferation of literature on methods to promote diversity in search and recommendation. However, diversity-aware studies in retrieval systems lack a systematic organization and are rather fragmented. In this survey, we are the first to propose a unified taxonomy for classifying the metrics and approaches of diversification in both search and recommendation, which are two of the most extensively researched fields of retrieval systems. We begin the survey with a brief discussion of why diversity is important in retrieval systems, followed by a summary of the various diversity concerns in search and recommendation, highlighting their relationship and differences. For the survey's main body, we present a unified taxonomy of diversification metrics and approaches in retrieval systems, from both the search and recommendation perspectives. In the later part of the survey, we discuss the open research questions of diversity-aware research in search and recommendation in an effort to inspire future innovations and encourage the implementation of diversity in real-world systems.Comment: 20 page

Density-based User Representation through Gaussian Process Regression for Multi-interest Personalized Retrieval

Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, like single-point and multi-point representations, have limitations w.r.t. accuracy, diversity, computational cost, and adaptability. To overcome these deficiencies, we introduce density-based user representations (DURs), a novel model that leverages Gaussian process regression for effective multi-interest recommendation and retrieval. Our approach, GPR4DUR, exploits DURs to capture user interest variability without manual tuning, incorporates uncertainty-awareness, and scales well to large numbers of users. Experiments using real-world offline datasets confirm the adaptability and efficiency of GPR4DUR, while online experiments with simulated users demonstrate its ability to address the exploration-exploitation trade-off by effectively utilizing model uncertainty.Comment: 16 pages, 5 figure

Teacher-Student Architecture for Knowledge Distillation: A Survey

Although Deep neural networks (DNNs) have shown a strong capacity to solve large-scale problems in many areas, such DNNs are hard to be deployed in real-world systems due to their voluminous parameters. To tackle this issue, Teacher-Student architectures were proposed, where simple student networks with a few parameters can achieve comparable performance to deep teacher networks with many parameters. Recently, Teacher-Student architectures have been effectively and widely embraced on various knowledge distillation (KD) objectives, including knowledge compression, knowledge expansion, knowledge adaptation, and knowledge enhancement. With the help of Teacher-Student architectures, current studies are able to achieve multiple distillation objectives through lightweight and generalized student networks. Different from existing KD surveys that primarily focus on knowledge compression, this survey first explores Teacher-Student architectures across multiple distillation objectives. This survey presents an introduction to various knowledge representations and their corresponding optimization objectives. Additionally, we provide a systematic overview of Teacher-Student architectures with representative learning algorithms and effective distillation schemes. This survey also summarizes recent applications of Teacher-Student architectures across multiple purposes, including classification, recognition, generation, ranking, and regression. Lastly, potential research directions in KD are investigated, focusing on architecture design, knowledge quality, and theoretical studies of regression-based learning, respectively. Through this comprehensive survey, industry practitioners and the academic community can gain valuable insights and guidelines for effectively designing, learning, and applying Teacher-Student architectures on various distillation objectives.Comment: 20 pages. arXiv admin note: substantial text overlap with arXiv:2210.1733

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Investigation of energy and operation flexibility of membrane bioreactors by using benchmark simulation model

The 6th MEMTEK International Syposium on Membrane Technologies and Applications, Istanbul, Turkey, 18-20 November 2019The aims of this study is to investigate operation and energy flexibility of membrane bioreactors for municipal wastewater treatment by mathematical modelling. Compared to conventional active sludge technology, membrane bioreactor has better treatment performance and it can achieve complete retention of solids and very high COD removal. Based on variable electricity price structure, appropriate optimization strategy can save 16% energy cost without violating exiting discharge standards.. The results showed that MBRs have a significant potential to create considerable commercial value by providing energetic flexibility.Science Foundation Irelan

Identifying Technology Opportunity Using SAO Semantic Mining and Outlier Detection Method: A Case of Triboelectric Nanogenerator Technology

With the high integration of science and technology development, how to early identify technology opportunity is crucial for the governments’ and enterprises’ research and development (R&D) strategic planning and innovation policy to gain a first-mover advantage in the market competition environment. Most researchers have applied Subject-Action-Object (SAO) semantic mining approach or outlier detection method to mine scientific papers or patent information for identifying technology opportunity. However, few researchers have combined information from both scientific papers and patents to identify technology opportunity by integrating SAO semantic mining and outlier detection method. Therefore, this paper proposes a research framework that uses scientific papers and patents as data resources, and integrates SAO semantic mining and outlier detection method to identify technology opportunity. In this framework, we first use the SAO semantic mining method to mine technical problems and solutions contained in scientific papers and patents respectively. Then we conduct comparative analysis to identify potential technology opportunity in the gaps between scientific papers and patents. Secondly, we use a outlier detection method to identify outlier points in scientific papers, and we incorporate the outlier points into the analysis scope of technology opportunity identification. Finally, we combine the results of SAO semantic mining method with outlier detection method, and use expert knowledge to identify technology opportunity. The triboelectric nanogenerator technology is selected as a case study to verify the feasibility of this framework. The results show that the framework can effectively and comprehensively identify technology opportunity from the two levels of technical problems and technical solutions. This paper contributes to technology opportunity study, and will be of interest to triboelectric nanogenerator technology R&D experts

Improved Oriented Object Detection in Remote Sensing Images Based on a Three-Point Regression Method

Object detection in remote sensing images plays an important role in both military and civilian remote sensing applications. Objects in remote sensing images are different from those in natural images. They have the characteristics of scale diversity, arbitrary directivity, and dense arrangement, which causes difficulties in object detection. For objects with a large aspect ratio and that are oblique and densely arranged, using an oriented bounding box can help to avoid deleting some correct detection bounding boxes by mistake. The classic rotational region convolutional neural network (R2CNN) has advantages for text detection. However, R2CNN has poor performance in the detection of slender objects with arbitrary directivity in remote sensing images, and its fault tolerance rate is low. In order to solve this problem, this paper proposes an improved R2CNN based on a double detection head structure and a three-point regression method, namely, TPR-R2CNN. The proposed network modifies the original R2CNN network structure by applying a double fully connected (2-fc) detection head and classification fusion. One detection head is for classification and horizontal bounding box regression, the other is for classification and oriented bounding box regression. The three-point regression method (TPR) is proposed for oriented bounding box regression, which determines the positions of the oriented bounding box by regressing the coordinates of the center point and the first two vertices. The proposed network was validated on the DOTA-v1.5 and HRSC2016 datasets, and it achieved a mean average precision (mAP) of 3.90% and 15.27%, respectively, from feature pyramid network (FPN) baselines with a ResNet-50 backbone

A Method For Part Analysis And Selection In The Renewal Process Of Product Family Architecture

Conference Name:3rd International Conference on Manufacturing Science and Engineering (ICMSE 2012). Conference Address: Xiamen, PEOPLES R CHINA. Time:MAR 27-29, 2012.Aiming at selecting appropriate parts in the product family architecture for innovating, a method for part analysis and selection in the renewal process of product family architecture is initially proposed. On the basis of parts analysis from the three aspects of cost effectiveness-commonality, performance sensitivity and demand-matching degree, a coupling spatial model CSM is established. With the fuzzy c-means clustering method, the parts were clustered, and the choice domain and the improvement direction were pointed out. Finally, a case study of part analysis and selection in the working device product family of the wheel loader was presented to illustrate the validity of the method