29 research outputs found
Towards Group-aware Search Success
Traditional measures of search success often overlook the varying information
needs of different demographic groups. To address this gap, we introduce a
novel metric, named Group-aware Search Success (GA-SS). GA-SS redefines search
success to ensure that all demographic groups achieve satisfaction from search
outcomes. We introduce a comprehensive mathematical framework to calculate
GA-SS, incorporating both static and stochastic ranking policies and
integrating user browsing models for a more accurate assessment. In addition,
we have proposed Group-aware Most Popular Completion (gMPC) ranking model to
account for demographic variances in user intent, aligning more closely with
the diverse needs of all user groups. We empirically validate our metric and
approach with two real-world datasets: one focusing on query auto-completion
and the other on movie recommendations, where the results highlight the impact
of stochasticity and the complex interplay among various search success
metrics. Our findings advocate for a more inclusive approach in measuring
search success, as well as inspiring future investigations into the quality of
service of search
Examining the Role of Peer Acknowledgements on Social Annotations: Unraveling the Psychological Underpinnings
This study explores the impact of peer acknowledgement on learner engagement
and implicit psychological attributes in written annotations on an online
social reading platform. Participants included 91 undergraduates from a large
North American University. Using log file data, we analyzed the relationship
between learners' received peer acknowledgement and their subsequent annotation
behaviours using cross-lag regression. Higher peer acknowledgements correlate
with increased initiation of annotations and responses to peer annotations. By
applying text mining techniques and calculating Shapley values to analyze 1,969
social annotation entries, we identified prominent psychological themes within
three dimensions (i.e., affect, cognition, and motivation) that foster peer
acknowledgment in digital social annotation. These themes include positive
affect, openness to learning and discussion, and expression of motivation. The
findings assist educators in improving online learning communities and provide
guidance to technology developers in designing effective prompts, drawing from
both implicit psychological cues and explicit learning behaviours
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
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
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
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