| 1. |
- Bodduluri, Kailash Chowdary, et al.
(författare)
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Exploring the Landscape of Hybrid Recommendation Systems in E-commerce : A Systematic Literature Review
- 2024
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 12, s. 28273-28296
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Forskningsöversikt (refereegranskat)abstract
- This article presents a systematic literature review on hybrid recommendation systems (HRS) in the e-commerce sector, a field characterized by constant innovation and rapid growth. As the complexity and volume of digital data increases, recommendation systems have become essential in guiding customers to services or products that align with their interests. However, the effectiveness of single-architecture recommendation algorithms is often limited by issues such as data sparsity, challenges in understanding user needs, and the cold start problem. Hybridization, which combines multiple algorithms in different methods, has emerged as a dominant solution to these limitations. This approach is utilized in various domains, including e-commerce, where it significantly improves user experience and sales. To capture the recent trends and advancements in HRS within e-commerce over the past six years, we review the state-of-the-art overview of HRS within e-commerce. This review meticulously evaluates existing research, addressing primary inquiries and presenting findings that contribute to evidence-based decision-making, understanding research gaps, and maintaining transparency. The review begins by establishing fundamental concepts, followed by detailed methodologies, findings from addressing the research questions, and exploration of critical aspects of HRS. In summarizing and incorporating existing research, this paper offers valuable insights for researchers and outlines potential avenues for future research, ultimately providing a comprehensive overview of the current state and prospects of HRS in e-commerce.
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| 2. |
- Bodduluri, Kailash Chowdary, et al.
(författare)
-
Exploring the Landscape of Hybrid Recommendation Systems in E-Commerce : A Systematic Literature Review
- 2024
-
Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 12, s. 28273-28296
-
Forskningsöversikt (refereegranskat)abstract
- This article presents a systematic literature review on hybrid recommendation systems (HRS) in the e-commerce sector, a field characterized by constant innovation and rapid growth. As the complexity and volume of digital data increases, recommendation systems have become essential in guiding customers to services or products that align with their interests. However, the effectiveness of single-architecture recommendation algorithms is often limited by issues such as data sparsity, challenges in understanding user needs, and the cold start problem. Hybridization, which combines multiple algorithms in different methods, has emerged as a dominant solution to these limitations. This approach is utilized in various domains, including e-commerce, where it significantly improves user experience and sales. To capture the recent trends and advancements in HRS within e-commerce over the past six years, we review the state-of-the-art overview of HRS within e-commerce. This review meticulously evaluates existing research, addressing primary inquiries and presenting findings that contribute to evidence-based decision-making, understanding research gaps, and maintaining transparency. The review begins by establishing fundamental concepts, followed by detailed methodologies, findings from addressing the research questions, and exploration of critical aspects of HRS. In summarizing and incorporating existing research, this paper offers valuable insights for researchers and outlines potential avenues for future research, ultimately providing a comprehensive overview of the current state and prospects of HRS in e-commerce.
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| 3. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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A survey of surveys on the use of visualization for interpreting machine learning models
- 2020
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Ingår i: Information Visualization. - : Sage Publications. - 1473-8716 .- 1473-8724. ; 19:3, s. 207-233
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Tidskriftsartikel (refereegranskat)abstract
- Research in machine learning has become very popular in recent years, with many types of models proposed to comprehend and predict patterns and trends in data originating from different domains. As these models get more and more complex, it also becomes harder for users to assess and trust their results, since their internal operations are mostly hidden in black boxes. The interpretation of machine learning models is currently a hot topic in the information visualization community, with results showing that insights from machine learning models can lead to better predictions and improve the trustworthiness of the results. Due to this, multiple (and extensive) survey articles have been published recently trying to summarize the high number of original research papers published on the topic. But there is not always a clear definition of what these surveys cover, what is the overlap between them, which types of machine learning models they deal with, or what exactly is the scenario that the readers will find in each of them. In this article, we present a metaanalysis (i.e. a ‘‘survey of surveys’’) of manually collected survey papers that refer to the visual interpretation of machine learning models, including the papers discussed in the selected surveys. The aim of our article is to serve both as a detailed summary and as a guide through this survey ecosystem by acquiring, cataloging, and presenting fundamental knowledge of the state of the art and research opportunities in the area. Our results confirm the increasing trend of interpreting machine learning with visualizations in the past years, and that visualization can assist in, for example, online training processes of deep learning models and enhancing trust into machine learning. However, the question of exactly how this assistance should take place is still considered as an open challenge of the visualization community.
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| 4. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
- 2020
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Ingår i: Computer graphics forum (Print). - : John Wiley & Sons. - 0167-7055 .- 1467-8659. ; 39:3, s. 713-756
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning (ML) models are nowadays used in complex applications in various domains such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web-based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.
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| 5. |
- Chatzimparmpas, Angelos
(författare)
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Visual Analytics for Explainable and Trustworthy Machine Learning
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The deployment of artificial intelligence solutions and machine learning research has exploded in popularity in recent years, with numerous types of models proposed to interpret and predict patterns and trends in data from diverse disciplines. However, as the complexity of these models grows, it becomes increasingly difficult for users to evaluate and rely on the model results, since their inner workings are mostly hidden in black boxes, which are difficult to trust in critical decision-making scenarios. While automated methods can partly handle these problems, recent research findings suggest that their combination with innovative methods developed within information visualization and visual analytics can lead to further insights gained from models and, consequently, improve their predictive ability and enhance trustworthiness in the entire process. Visual analytics is the area of research that studies the analysis of vast and intricate information spaces by combining statistical and machine learning models with interactive visual interfaces. By following this methodology, human experts can better understand such spaces and apply their domain expertise in the process of building and improving the underlying models.The primary goals of this dissertation are twofold, focusing on (1) methodological aspects, by conducting qualitative and quantitative meta-analyses to support the visualization research community in making sense of its literature and to highlight unsolved challenges, as well as (2) technical solutions, by developing visual analytics approaches for various machine learning models, such as dimensionality reduction and ensemble learning methods. Regarding the first goal, we define, categorize, and examine in depth the means for visual coverage of the different trust levels at each stage of a typical machine learning pipeline and establish a design space for novel visualizations in the area. Regarding the second goal, we discuss multiple visual analytics tools and systems implemented by us to facilitate the underlying research on the various stages of the machine learning pipeline, i.e., data processing, feature engineering, hyperparameter tuning, understanding, debugging, refining, and comparing models. Our approaches are data-agnostic, but mainly target tabular data with meaningful attributes in diverse domains, such as health care and finance. The applicability and effectiveness of this work were validated with case studies, usage scenarios, expert interviews, user studies, and critical discussions of limitations and alternative designs. The results of this dissertation provide new avenues for visual analytics research in explainable and trustworthy machine learning.
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| 6. |
- Giménez, Alfredo, et al.
(författare)
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Dissecting On-node Memory Access Performance : A Semantic Approach
- 2014
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Ingår i: SC14: International Conference for High Performance Computing, Networking, Storage and Analysis. - Piscataway, NJ, USA : IEEE Press. ; , s. 166-176, s. 166-176
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Konferensbidrag (refereegranskat)abstract
- Optimizing memory access is critical for performance and power efficiency. CPU manufacturers have developed sampling-based performance measurement units (PMUs) that report precise costs of memory accesses at specific addresses. However, this data is too low-level to be meaningfully interpreted and contains an excessive amount of irrelevant or uninteresting information.We have developed a method to gather fine-grained memory access performance data for specific data objects and regions of code with low overhead and attribute semantic information to the sampled memory accesses. This information provides the context necessary to more effectively interpret the data. We have developed a tool that performs this sampling and attribution and used the tool to discover and diagnose performance problems in real-world applications. Our techniques provide useful insight into the memory behavior of applications and allow programmers to understand the performance ramifications of key design decisions: domain decomposition, multi-threading, and data motion within distributed memory systems.
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| 7. |
- Gimenez, Alfredo, et al.
(författare)
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MemAxes : Visualization and Analytics for Characterizing Complex Memory Performance Behaviors
- 2018
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Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : IEEE. - 1077-2626 .- 1941-0506. ; 24:7, s. 2180-2193
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Tidskriftsartikel (refereegranskat)abstract
- Memory performance is often a major bottleneck for high-performance computing (HPC) applications. Deepening memory hierarchies, complex memory management, and non-uniform access times have made memory performance behavior difficult to characterize, and users require novel, sophisticated tools to analyze and optimize this aspect of their codes. Existing tools target only specific factors of memory performance, such as hardware layout, allocations, or access instructions. However, today's tools do not suffice to characterize the complex relationships between these factors. Further, they require advanced expertise to be used effectively. We present MemAxes, a tool based on a novel approach for analytic-driven visualization of memory performance data. MemAxes uniquely allows users to analyze the different aspects related to memory performance by providing multiple visual contexts for a centralized dataset. We define mappings of sampled memory access data to new and existing visual metaphors, each of which enabling a user to perform different analysis tasks. We present methods to guide user interaction by scoring subsets of the data based on known performance problems. This scoring is used to provide visual cues and automatically extract clusters of interest. We designed MemAxes in collaboration with experts in HPC and demonstrate its effectiveness in case studies.
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| 8. |
- Gleicher, Michael, et al.
(författare)
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Visual Comparison for Information Visualization
- 2011
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Ingår i: Information Visualization. - : SAGE Publications. - 1473-8716 .- 1473-8724. ; 10:5-4, s. 289-309
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Tidskriftsartikel (refereegranskat)abstract
- Data analysis often involves the comparison of complex objects. With the ever increasing amounts and complexity of data, the demand for systems to help with these comparisons is also growing. Increasingly, information visualization tools support such comparisons explicitly, beyond simply allowing a viewer to examine each object individually. In this paper, we argue that the design of information visualizations of complex objects can, and should, be studied in general, that is independently of what those objects are. As a first step in developing this general understanding of comparison, we propose a general taxonomy of visual designs for comparison that groups designs into three basic categories, which can be combined. To clarify the taxonomy and validate its completeness, we provide a survey of work in information visualization related to comparison. Although we find a great diversity of systems and approaches, we see that all designs are assembled from the building blocks of juxtaposition, superposition and explicit encodings. This initial exploration shows the power of our model, and suggests future challenges in developing a general understanding of comparative visualization and facilitating the development of more comparative visualization tools.
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| 9. |
- Golub, Koraljka, et al.
(författare)
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Automatic subject classification for improving retrieval in a Swedish repository
- 2017
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Ingår i: ISKO UK Conference 2017: Knowledge Organization: what's the story?, 11 – 12 September 2017, London.
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Konferensbidrag (refereegranskat)abstract
- The recent adoption of the Dewey Decimal Classification (DDC) in Sweden has ignited discussions about automated subject classification especially for digital collections, which generally seem to lack subject indexing from controlled vocabularies. This is particularly problematic in the context of academic resource retrieval tasks, which require an understanding of discipline-specific terminologies and the narratives behind their internal ontologies. The currently available experimental classification software have not been adequately tested and their usefulness is unproven especially for Swedish language resources. We address these issues by investigating a unifying framework of automatic subject indexing for the DDC, including an analysis of suitable interactive visualisation features for supporting these aims. We will address the disciplinary narratives behind the DDC in selected subject areas and the preliminary results will include an analysis of the data collection and a breakdown of the methodology. Major visualisation possibilities in support of the classification process are also outlined. The project will contribute significantly to Swedish information infrastructure by improving the findability of Swedish research resources by subject searching, one of the most common yet the most challenging types of searching.
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| 10. |
- Isaacs, Katherine, et al.
(författare)
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Combing the Communication Hairball: Visualizing Parallel Execution Traces using Logical Time
- 2014
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Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : IEEE Press. - 1077-2626 .- 1941-0506. ; 20:12, s. 2349-2358
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Tidskriftsartikel (refereegranskat)abstract
- With the continuous rise in complexity of modern supercomputers, optimizing the performance of large-scale parallel programs is becoming increasingly challenging. Simultaneously, the growth in scale magnifies the impact of even minor inefficiencies - potentially millions of compute hours and megawatts in power consumption can be wasted on avoidable mistakes or sub-optimal algorithms. This makes performance analysis and optimization critical elements in the software development process. One of the most common forms of performance analysis is to study execution traces, which record a history of per-process events and interprocess messages in a parallel application. Trace visualizations allow users to browse this event history and search for insights into the observed performance behavior. However, current visualizations are difficult to understand even for small process counts and do not scale gracefully beyond a few hundred processes. Organizing events in time leads to a virtually unintelligible conglomerate of interleaved events and moderately high process counts overtax even the largest display. As an alternative, we present a new trace visualization approach based on transforming the event history into logical time inferred directly from happened-before relationships. This emphasizes the code’s structural behavior, which is much more familiar to the application developer. The original timing data, or other information, is then encoded through color, leading to a more intuitive visualization. Furthermore, we use the discrete nature of logical timelines to cluster processes according to their local behavior leading to a scalable visualization of even long traces on large process counts. We demonstrate our system using two case studies on large-scale parallel codes.
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