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Fast and Frictionless: A Novel Approach to Radiology Appointment Scheduling Using a Mobile App and recommendation Engine

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JOURNAL OF DIGITAL IMAGING 2023年第4期36卷 1285-1290页

作者： Doshi, Ankur M. Ostrow, Dana Gresens, August Grimmelmann, Rachel Mazhar, Salman Neto, Eduardo Woodriff, Molly Recht, Michael NYU Langone Hlth Dept Radiol 660 First Ave3rd Floor New York NY 10016 USA NYU Langone Hlth Med Ctr IT One Pk Ave New York NY 10016 USA

Many outpatient radiology orders are never scheduled, which can result in adverse outcomes. Digital appointment self-scheduling provides convenience, but utilization has been low. The purpose of this study was to develop a "frictionless" scheduling tool and evaluate the impact on utilization. The existing institutional radiology scheduling app was configured to accommodate a frictionless workflow. A recommendation engine used patient residence, past and future appointment data to generate three optimal appointment suggestions. For eligible frictionless orders, recommendations were sent in a text message. Other orders received either a text message for the non-frictionless app scheduling approach or a call-to-schedule text. Scheduling rates by type of text message and scheduling workflow were analyzed. Baseline data for a 3-month period prior to the launch of frictionless scheduling showed that 17% of orders that received an order notification text were scheduled using the app. In an 11-month period after the launch of frictionless scheduling, the rate of app scheduling was greater for orders that received a text message with recommendations (frictionless approach) versus app schedulable orders that received a text without recommendations (29% vs. 14%, p < 0.01). Thirty-nine percent of the orders that received a frictionless text and scheduled using the app used a recommendation. The most common recommendation rules chosen for scheduling included location preference of prior appointments (52%). Among appointments that were scheduled using a day or time preference, 64% were based on a rule using the time of the day. This study showed that frictionless scheduling was associated with an increased rate of app scheduling.

关键词： Digital appointment scheduling recommendation algorithms Frictionless scheduling

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A state-of-the-art survey on recommendation system and prospective extensions

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2020年 178卷 105779-105779页

作者： Patel, Krupa Patel, Hiren B. Kadi Sarva Vishwavidhyalaya Gandhinagar Gujarat India

With the new era of the Internet, we have a large amount of data available in the form of ratings, reviews, graphs, images, etc. However, still, people face difficulty in finding useful information or knowledge from those data. To address these challenges, recommendation systems come into the picture by providing useful content to the user based on users' history and similarity among users. Content-based and collaborative filtering are two major building blocks of recommendation systems. recommendation systems have been applied into numbers of a domain such as recommending movies, music, course, literature, items, people, links, location, healthcare, agriculture. In the agriculture domain, appropriate crops to cultivate and selecting applicable pesticides based on land quality and types of crops are interesting factors to consider for a country like India. Initially, we review different types of recommendation systems along with its application area. Subsequently, we explore various parameters to evaluate recommendation systems followed by open issues and research challenges. We further study the work carried out by existing researchers in the said domain. As part of our contribution through this research, we have selected the Agriculture domain and proposed our algorithm for recommending crops based on various parameters. As an outcome of our contribution, a crop is recommended to farmers based on his land. Also, the system recommends a list of lands for a given crop. Using statistical analysis, we achieve accuracy from 93% to 97%.

关键词： Content-base Collaborative filtering Crop recommendation system recommendation algorithms Agriculture

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Scientific Paper recommendation: A Survey

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IEEE ACCESS 2019年 7卷 9324-9339页

作者： Bai, Xiaomei Wang, Mengyang Lee, Ivan Yang, Zhuo Kong, Xiangjie Xia, Feng Anshan Normal Univ Comp Ctr Anshan 114007 Peoples R China Dalian Univ Technol Sch Software Dalian 116620 Peoples R China Univ South Australia Sch Informat Technol & Math Sci Adelaide SA 5001 Australia

Globally, the recommendation services have become important due to the fact that they support e-commerce applications and different research communities. Recommender systems have a large number of applications in many fields, including economic, education, and scientific research. Different empirical studies have shown that the recommender systems are more effective and reliable than the keyword-based search engines for extracting useful knowledge from massive amounts of data. The problem of recommending similar scientific articles in scientific community is called scientific paper recommendation. Scientific paper recommendation aims to recommend new articles or classical articles that match researchers' interests. It has become an attractive area of study since the number of scholarly papers increases exponentially. In this paper, we first introduce the importance and advantages of the paper recommender systems. Second, we review the recommendation algorithms and methods, such as Content-based, collaborative filtering, graph-based, and hybrid methods. Then, we introduce the evaluation methods of different recommender systems. Finally, we summarize the open issues in the paper recommender systems, including cold start, sparsity, scalability, privacy, serendipity, and unified scholarly data standards. The purpose of this survey is to provide comprehensive reviews on the scholarly paper recommendation.

关键词： Recommender systems scientific paper recommendation recommendation algorithms

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Empirical and Experimental Perspectives on Big Data in recommendation Systems:A Comprehensive Survey

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Big Data Mining and Analytics 2024年第3期7卷 964-1014页

作者： Kamal Taha Paul D.Yoo Chan Yeun Aya Taha Department of Computer Science Khalifa UniversityAbu Dhabi 127788United Arab Emirates(UAE) School of Computing and Mathematical Sciences Birkbeck CollegeUniversity of LondonLondon WC1E 7HUUK Department of Science Brighton CollegeDubai 122002United Arab Emirates

This survey paper provides a comprehensive analysis of big data algorithms in recommendation systems,addressing the lack of depth and precision in existing *** proposes a two-pronged approach:a thorough analysis of current algorithms and a novel,hierarchical taxonomy for precise *** taxonomy is based on a tri-level hierarchy,starting with the methodology category and narrowing down to specific *** a framework allows for a structured and comprehensive classification of algorithms,assisting researchers in understanding the interrelationships among diverse algorithms and *** a wide range of algorithms,this taxonomy first categorizes algorithms into four main analysis types:user and item similarity based methods,hybrid and combined approaches,deep learning and algorithmic methods,and mathematical modeling methods,with further subdivisions into sub-categories and *** paper incorporates both empirical and experimental evaluations to differentiate between the *** empirical evaluation ranks the techniques based on four *** experimental assessments rank the algorithms that belong to the same category,sub-category,technique,and ***,the paper illuminates the future prospects of big data techniques in recommendation systems,underscoring potential advancements and opportunities for further research in this fields.

关键词： big data algorithms recommendation systems recommendation algorithms deep learning in recommendations

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recommendation in feature space sphere

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ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 2017年 26卷 109-118页

作者： Zhao, Zhi-Lin Wang, Chang-Dong Wan, Yuan-Yu Lai, Jian-Huang Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China Nanjing Univ Dept Comp Sci & Technol Nanjing Jiangsu Peoples R China

Recently, recommendation algorithms have been widely used in many e-commerce platforms to recommend items to users on the basis of their preferences to improve selling efficiency. Matrix factorization methods which extract latent features of users and items by decomposing the rating matrix have achieved success in rating prediction. But almost all of these algorithms are designed to fit the rating matrix directly to get the latent features and ignore the user-item relationship in feature space. To this end, in this paper, we propose a recommendation in feature space sphere (RFSS) which takes into account the relationship between users and items in feature space. Different from the conventional latent feature based recommendation algorithms, the proposed algorithm supposes that if a user likes an item, the user is close to the item in feature space. Meanwhile, the closer a user and an item are in feature space, the higher the predicted rating will be. And an adaptive user-dependent coefficient is introduced to map the user-item distances to the predicted ratings. Extensive experiments on four real-world datasets have been conducted, the results of which show that our proposed method outperforms the state-of-the-art recommendation algorithms. (C) 2017 Elsevier B.V. All rights reserved.

关键词： recommendation algorithms Matrix factorization Feature space

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A Naive Bayes prediction model on location-based recommendation by integrating multi-dimensional contextual information

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第5期81卷 6957-6978页

作者： Gultekin, Gunay Bayat, Oguz Altinbas Univ Elect & Comp Engn Istanbul Turkey

In recent years, researchers have been trying to create recommender systems. There are many different recommender systems. Point of Interest (POI) is a new type of recommender systems that focus on personalized and context-aware recommendations to improve user experience. Recommender systems use different types of recommendation methods to obtain information on POI. In this research paper, we introduced a Naive Bayes Prediction Model based on Bayesian Theory for POI recommendation. Then, we used the Brightkite dataset to make predictions on POI recommendation and compared it with the other two different recommendation methods. Experimental results confirm that our proposed method outperforms on Location-based POI recommendation.

关键词： recommendation algorithms Collaborative filtering Factorization Big data analysis Location-based social networks Naive Bayes theorem

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PREFERENTIAL ATTACHMENT, AGING AND WEIGHTS IN recommendation SYSTEMS

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INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS 2009年第2期19卷 755-763页

作者： Zanin, Massimiliano Cano, Pedro Celma, Oscar Buldu, Javier M. Univ Pompeu Fabra Mus Technol Grp Barcelona 08003 Spain BMAT Barcelona 08018 Spain Univ Rey Juan Carlos Complex Syst Grp Madrid 28933 Spain

In the present work, algorithms based on complex network theory are applied to recommendation Systems in order to improve their quality of predictions. We show how some networks are grown under the influence of trendy forces, and how this can be used to enhance the results of a recommendation system, i.e. increase their percentage of right predictions. After de. ning a base algorithm, we create recommendation networks which are based on a histogram of user ratings, using therefore an underlying principle of preferential attachment. We show the influence of data aging in the prediction of user habits and how the exact moment of the prediction influences the recommendation. Finally, we design weighted networks that take into account the age of the information used to generate the links. In this way, we obtain a better approximation to evaluate the users' tastes.

关键词： Complex networks recommendation algorithms aging music networks

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An Efficient Service recommendation Algorithm for Cyber-Physical-Social Systems

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第6期9卷 3847-3859页

作者： Chen, Xiaoyan Liang, Wei Xu, Jianbo Wang, Chong Li, Kuan-Ching Qiu, Meikang Xiamen Univ Technol Sch Software Engn Xiamen 361024 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Peoples R China Providence Univ Dept Comp Sci & Informat Engr CSIE Shalu 43301 Taiwan Texas A&M Univ Commerce Dept Comp Sci Commerce TX 75428 USA

Cyber-physical-social System (CPSS) technology closely integrates and coordinates computing resources, physical resources, and social information to provide humans with efficient and convenient services. Service recommendation technology satisfies the individual needs of users by collecting, processing, and analyzing user characteristic data. In recent years, service recommendation algorithms based on social networks and collaborative filtering have been widely used in CPSS. However, this type of service recommendation algorithm cannot effectively use the aliasing and auxiliary information in the service, nor can it generate different service recommendation schemes according to different scenarios. We propose a collaborative filtering service recommendation algorithm that combines heterogeneous information networks and topic models for CPSS. The proposed algorithm designs a more effective functional similarity measurement method through the word vectors learned by Word2vec. Then, we use the topic model to cluster the scenario-based service (SBS), thereby greatly reducing the time required for service recommendation. Experimental analysis results show that we finally determine the best combination of Mashup similarity between the two scenarios with better recommendation efficiency and accuracy. Thus, the proposed recommendation algorithm can effectively improve the quality of service of CPSS, and provide a basis for the personalized service of CPSS.

关键词： Mashups Collaboration Service-oriented architecture Clustering algorithms Quality of service Cyber-physical systems Data models Social factors Recommender systems Cyber-physical-social systems (CPSS) recommendation algorithms scenarios based service (SBS) collaborative filtering

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An improved matrix factorization based model for many-objective optimization recommendation

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INFORMATION SCIENCES 2021年 579卷 1-14页

作者： Cui, Zhihua Zhao, Peng Hu, Zhaoming Cai, Xingjuan Zhang, Wensheng Chen, Jinjun Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Co Beijing Peoples R China Swinburne Univ Technol Melbourne Vic Australia

As the application scenarios of recommendation algorithms are becoming increasingly complex, the efficiency of traditional recommendation algorithm based on accuracy is no longer satisfied. To solve this problem, an improved matrix factorization based model for many-objective optimization recommendation is proposed to simultaneously optimize the four recommendation objectives of novelty, diversity, accuracy, and recall. As a novel double-layer recommendation model, two improved algorithms are composed: 1) For the bottom layer, an improved matrix factorization algorithm with additional regulariza-tion constraints is used to predict unknown item ratings;2) For the top layer, the recom-mendation list is optimized by a many-objective evolutionary algorithm. Comprehensive experiments demonstrate that the proposed model can effectively improve the four recom-mended evaluation metrics. And a recommended list with novel and diverse items is pro-vided for users in a more efficient way while maintaining accuracy. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Many-objective evolutionary algorithms recommendation algorithms Novel item recommendation Matrix factorization

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Personalized recommendation based on review topics

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SERVICE ORIENTED COMPUTING AND APPLICATIONS 2014年第1期8卷 15-31页

作者： Zheng, Xiaolin Ding, Weifeng Xu, Jingnan Chen, Deren Zhejiang Univ Dept Comp Sci Hangzhou Zhejiang Peoples R China

The traditional collaborative filtering algorithm is a successful recommendation technology. The core idea of this algorithm is to calculate user or item similarity based on user ratings and then to predict ratings and recommend items based on similar users' or similar items' ratings. However, real applications face a problem of data sparsity because most users provide only a fewratings, such that the traditional collaborative filtering algorithm cannot produce satisfactory results. This paper proposes a new topic model-based similarity and two recommendation algorithms: user-based collaborative filtering with topic model algorithm (UCFTM, in this paper) and item-based collaborative filtering with topic model algorithm (ICFTM, in this paper). Each review is processed using the topicmodel to generate reviewtopic allocations representing a user's preference for a product's different features. The UCFTM algorithm aggregates all topic allocations of reviews by the same user and calculates the user most valued features representing product features that the user most values. User similarity is calculated based on user most valued features, whereas ratings are predicted from similar users' ratings. The ICFTM algorithm aggregates all topic allocations of reviews for the same product, and item most valued features representing the most valued features of the product are calculated. Item similarity is calculated based on item most valued features, whereas ratings are predicted from similar items' ratings. Experiments on six data sets from Amazon indicate that when most users give only one review and one rating, our algorithms exhibit better prediction accuracy than other traditional collaborative filtering and state-of-the-art topic model-based recommendation algorithms.

关键词： Collaborative filtering recommendation algorithms Topic model Sparsity problem

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