Collaborative filtering for music recommender system

Shakirova, E. I.

doi:10.1109/eiconrus.2017.7910613

Cited by 35 publications

(9 citation statements)

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“…Content-based RS or cognitive RS provides recommendations based on a comparison of the items’ content with a user profile [ 53 , 54 ]. Collaborative RS collects preferences or taste information from the collaborated users to produce automatic predictions regarding the user’s interests [ 55 , 56 ]. Memory-based and model-based are the two different categories of a collaborative RS.…”

Section: Methodsmentioning

confidence: 99%

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity

et al. 2022

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Background: Hospitals face a significant problem meeting patients’ medical needs during epidemics, especially when the number of patients increases rapidly, as seen during the recent COVID-19 pandemic. This study designs a treatment recommender system (RS) for the efficient management of human capital and resources such as doctors, medicines, and resources in hospitals. We hypothesize that a deep learning framework, when combined with search paradigms in an image framework, can make the RS very efficient. Methodology: This study uses a Convolutional neural network (CNN) model for the feature extraction of the images and discovers the most similar patients. The input queries patients from the hospital database with similar chest X-ray images. It uses a similarity metric for the similarity computation of the images. Results: This methodology recommends the doctors, medicines, and resources associated with similar patients to a COVID-19 patients being admitted to the hospital. The performance of the proposed RS is verified with five different feature extraction CNN models and four similarity measures. The proposed RS with a ResNet-50 CNN feature extraction model and Maxwell–Boltzmann similarity is found to be a proper framework for treatment recommendation with a mean average precision of more than 0.90 for threshold similarities in the range of 0.7 to 0.9 and an average highest cosine similarity of more than 0.95. Conclusions: Overall, an RS with a CNN model and image similarity is proven as an efficient tool for the proper management of resources during the peak period of pandemics and can be adopted in clinical settings.

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Section: Methodsmentioning

confidence: 99%

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity

et al. 2022

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“…The form-giving process will be inspired by Di Mari and Yoo's grammar of physical forms to guide the practical form giving processes [24], and product semantics to design physical forms in relation to other artifacts and the context of use. We will consider RS techniques such as collaborative filtering; content-based filtering [29]; hybrid filtering and critique-based RS techniques [6] as well as culture-aware and context-aware RS [26] to inform design process. We further bring into discussions the issues related to RS, such as filter bubbles, gender bias [10] and noise in data, among others, to generate ideas.…”

Section: Second Partmentioning

confidence: 99%

Graspable AI: Physical Forms as Explanation Modality for Explainable AI

Ghajargar

Bardzell

Smith-Renner³

et al. 2022

Sixteenth International Conference on Tangible, Embedded, and Embodied Interaction

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Explainable AI (XAI) seeks to disclose how an AI system arrives at its outcomes. But the nature of the disclosure depends in part on who needs to understand the AI and the available explanation modalities (e.g., verbal and visual). Users' preferences regarding explanation modalities might differ, as some might prefer spoken explanations compared to visual ones. However, we argue for broadening the explanation modalities, to consider also tangible and physical forms. In traditional product design, physical forms have mediated people's interactions with objects; more recently interacting with physical forms has become prominent with IoT and smart devices, such as smart lighting and robotic vacuum cleaners. But how tangible interaction can support AI explanations is not yet well understood.In this second studio proposal on Graspable AI (GAI) we seek to explore design qualities of physical forms [12] as an explanation modality for XAI. We anticipate that the design qualities of physical forms and their tangible interactivity can not only contribute to the explainability of AI through facilitating dialogue [5], relationships [18] and human empowerment [15], but they can also contribute to critical and reflective discourses on AI [2,13]. Therefore, this proposal contributes to design agendas that expand explainable AI into tangible modalities, supporting a more diverse range of users in their understanding of how a given AI works and the meanings of its outcomes. CCS CONCEPTS• Human-centered computing → Interactive systems and tools; Interaction design; Interaction design theory, concepts and paradigms;

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“…Collaborative filtering systems suggest the most appropriate music based on user behavior of a group of users [110]. This approach's idea is that whether a user X and Y similarly classify n items or have similar behavior, they will classify or act on other items in a similar way.…”

Section: Collaborative Filteringmentioning

confidence: 99%