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Статті в журналах з теми "Sequence-based recommender"
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Monti, Diego, Enrico Palumbo, Giuseppe Rizzo, and Maurizio Morisio. "Sequeval: An Offline Evaluation Framework for Sequence-Based Recommender Systems." Information 10, no. 5 (May 10, 2019): 174. http://dx.doi.org/10.3390/info10050174.
Повний текст джерелаАнотація:
Recommender systems have gained a lot of popularity due to their large adoption in various industries such as entertainment and tourism. Numerous research efforts have focused on formulating and advancing state-of-the-art of systems that recommend the right set of items to the right person. However, these recommender systems are hard to compare since the published evaluation results are computed on diverse datasets and obtained using different methodologies. In this paper, we researched and prototyped an offline evaluation framework called Sequeval that is designed to evaluate recommender systems capable of suggesting sequences of items. We provide a mathematical definition of such sequence-based recommenders, a methodology for performing their evaluation, and the implementation details of eight metrics. We report the lessons learned using this framework for assessing the performance of four baselines and two recommender systems based on Conditional Random Fields (CRF) and Recurrent Neural Networks (RNN), considering two different datasets. Sequeval is publicly available and it aims to become a focal point for researchers and practitioners when experimenting with sequence-based recommender systems, providing comparable and objective evaluation results.
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Shishehchi, Saman, Nor Azan Mat Zin, and Esmadi Abu Abu Seman. "Ontology-Based Recommender System for a Learning Sequence in Programming Languages." International Journal of Emerging Technologies in Learning (iJET) 16, no. 12 (June 18, 2021): 123. http://dx.doi.org/10.3991/ijet.v16i12.21451.
Повний текст джерелаАнотація:
The same learning process in educational systems could be boring and time consuming for some learners. This problem arises from the lack of personalized learning sequence for learners with different knowledge level. Recommender systems play an important role in assisting the learners to find suitable learning materials and personalized learning sequence. Use of ontology for knowledge representation in knowledge-based recommender systems would facilitate sharing, reuse and common terminology. Since programming concepts have logical relationships among together so, traditional education systems are more stressful and very time-consuming. This paper aims to propose an ontology based recommender system to present a Personalized Learning Sequence in Programming (PLSP) domain which is depended to learner's knowledge level. A recommender module and, the knowledge base module are integrated together in the proposed framework. The recommender module as the main module in the framework, has three stages which is working based on semantic rules and ontology representation. Evaluation of the system was carried out by comparing the non-recommender system (web-based search) using 32 ICT respondents. Results demonstrate that the participants who used the proposed system spent 1119 seconds to find the suitable learning path in comparison to those who used a non-recommender system (3480 seconds) in the same learning material. It means that learners who follow learning path with PLSP, are more suitable for them. Furthermore, the average mean value of usability test is 4.47, (5 maximum scale) which indicates that the system proved to be useful, was easy to use, and satisfied the users.
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Zhang, Qingsheng, Di Yang, Pengjun Fang, Nannan Liu, and Lu Zhang. "Develop Academic Question Recommender Based on Bayesian Network for Personalizing Student’s Practice." International Journal of Emerging Technologies in Learning (iJET) 15, no. 18 (September 25, 2020): 4. http://dx.doi.org/10.3991/ijet.v15i18.11594.
Повний текст джерелаАнотація:
Study in Literatures shows that tracing knowledge state of student is corner stone of intelligent tutoring system for personalized learning. In this paper, an academic question recommender based on Bayesian network is developed for personalizing practice question sequence with tracing mastery level of student on knowledge components. This question recommender is discussed with theoretical analysis, and designed and implemented in software engineering way. It provides instructor with tools for building knowledge component network and setting question of course. It also makes student personalize practice questions of course. This question recommender is planned to deploy in real learning context for the future validation of how well such question recommendation improves performance and saves practice time for student.
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Fang, Hui, Chongcheng Chen, Yunfei Long, Ge Xu, and Yongqiang Xiao. "DTCRSKG: A Deep Travel Conversational Recommender System Incorporating Knowledge Graph." Mathematics 10, no. 9 (April 22, 2022): 1402. http://dx.doi.org/10.3390/math10091402.
Повний текст джерелаАнотація:
In the era of information explosion, it is difficult for people to obtain their desired information effectively. In tourism, a travel recommender system based on big travel data has been developing rapidly over the last decade. However, most work focuses on click logs, visit history, or ratings, and dynamic prediction is absent. As a result, there are significant gaps in both dataset and recommender models. To address these gaps, in the first step of this study, we constructed two human-annotated datasets for the travel conversational recommender system. We provided two linked data sets, namely, interaction sequence and dialogue data sets. The usage of the former data set was done to fully explore the static preference characteristics of users based on it, while the latter identified the dynamics changes in user preference from it. Then, we proposed and evaluated BERT-based baseline models for the travel conversational recommender system and compared them with several representative non-conversational and conversational recommender system models. Extensive experiments demonstrated the effectiveness and robustness of our approach regarding conversational recommendation tasks. Our work can extend the scope of the travel conversational recommender system and our annotated data can also facilitate related research.
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Lee, Hea In, Il Young Choi, Hyun Sil Moon, and Jae Kyeong Kim. "A Multi-Period Product Recommender System in Online Food Market based on Recurrent Neural Networks." Sustainability 12, no. 3 (January 29, 2020): 969. http://dx.doi.org/10.3390/su12030969.
Повний текст джерелаАнотація:
A recommender system supports customers to find information, products, or services (such as music, books, movies, web sites, and digital contents), so it could help customers to make rapid routine decisions and save their time and money. However, most existing recommender systems do not recommend items that are already purchased by the target customer, so are not suitable for considering customers’ repetitive purchase behavior or purchasing order. In this research, we suggest a multi-period product recommender system, which can learn customers’ purchasing order and customers’ repetitive purchase pattern. For such a purpose we applied the Recurrent Neural Network (RNN), which is one of the artificial neural network structures specialized in time series data analysis, instead of collaborative filtering techniques. Recommendation periods are segmented as various time-steps, and the proposed RNN-based recommender system can recommend items by multiple periods in a time sequence. Several experiments with real online food market data show that the proposed system shows higher performance in accuracy and diversity in a multi-period perspective than the collaborative filtering-based system. From the experimental results, we conclude that the proposed system is suitable for multi-period product recommendation, which results in robust performance considering well customers’ purchasing orders and customers’ repetitive purchase patterns. Moreover, in terms of sustainability, we expect that our study contributes to the reduction of food wastes by inducing planned consumption, and the reduction of shopping time and effort.
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Wang, Wei, and Longbing Cao. "Interactive Sequential Basket Recommendation by Learning Basket Couplings and Positive/Negative Feedback." ACM Transactions on Information Systems 39, no. 3 (February 23, 2021): 1–26. http://dx.doi.org/10.1145/3444368.
Повний текст джерелаАнотація:
Sequential recommendation , such as next-basket recommender systems (NBRS), which model users’ sequential behaviors and the relevant context/session, has recently attracted much attention from the research community. Existing session-based NBRS involve session representation and inter-basket relations but ignore their hybrid couplings with the intra-basket items, often producing irrelevant or similar items in the next basket. In addition, they do not predict next-baskets (more than one next basket recommended). Interactive recommendation further involves user feedback on the recommended basket. The existing work on next-item recommendation involves positive feedback on selected items but ignores negative feedback on unselected ones. Here, we introduce a new setting— interactive sequential basket recommendation , which iteratively predicts next baskets by learning the intra-/inter-basket couplings between items and both positive and negative user feedback on recommended baskets. A hierarchical attentive encoder-decoder model (HAEM) continuously recommends next baskets one after another during sequential interactions with users after analyzing the item relations both within a basket and between adjacent sequential baskets (i.e., intra-/inter-basket couplings) and incorporating the user selection and unselection (i.e., positive/negative) feedback on the recommended baskets to refine NBRS. HAEM comprises a basket encoder and a sequence decoder to model intra-/inter-basket couplings and a prediction decoder to sequentially predict next-baskets by interactive feedback-based refinement. Empirical analysis shows that HAEM significantly outperforms the state-of-the-art baselines for NBRS and session-based recommenders for accurate and novel recommendation. We also show the effect of continuously refining sequential basket recommendation by including unselection feedback during interactive recommendation.
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Martínez-López, Francisco J., Irene Esteban-Millat, Ana Argila, and Francisco Rejón-Guardia. "Consumers’ psychological outcomes linked to the use of an online store’s recommendation system." Internet Research 25, no. 4 (August 3, 2015): 562–88. http://dx.doi.org/10.1108/intr-01-2014-0033.
Повний текст джерелаАнотація:
Purpose – Psychological perspective has been omitted or considered a secondary issue by past studies focused on e-commerce recommendation systems (RS). However, this perspective is key to gaining a better understanding of consumer behaviours when these systems are used to support purchasing processes at online stores. The paper aims to discuss these issues. Design/methodology/approach – The field study consisted of a simulated online shopping process undertaken by a sample of internet users with a recommender system at a real online store (Pixmania). The authors applied rigorous and detailed exploratory and confirmatory factor analyses to assess the empirical validity of the model. Findings – The proposed sequence of psychological outcomes is valid, with the exception of one hypothesized relationship. In particular, satisfaction with an online store’s recommender has a strong influence on a consumer’s willingness to purchase one of the items related to his/her shopping goal. However, this satisfaction has no direct effect on a consumer’s intention to make add-on purchases based on the recommender’s suggestions. On the contrary, the results support the idea that add-on purchases are conditioned by a previous purchase related to the consumer’s initial shopping goal. On the other hand, a consumer’s flow state while shopping improves all his/her psychological outcomes linked to an online store’s recommender. The influence of flow state is particularly interesting when seeking to gain a better understanding of consumers’ unplanned purchases based on the recommender’s suggestions. These findings have important implications for practitioners. Originality/value – This paper discusses in detail and empirically test a set of psychological outcomes that emerge when an e-vendor’s recommender is used to assist a consumer’s shopping process. To the best of the knowledge, this is the first attempt that empirically tests most of the hypothesized relationships within an online store’s RS context.
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Gu, Jiqing, Chao Song, Wenjun Jiang, Xiaomin Wang, and Ming Liu. "Enhancing Personalized Trip Recommendation with Attractive Routes." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 662–69. http://dx.doi.org/10.1609/aaai.v34i01.5407.
Повний текст джерелаАнотація:
Personalized trip recommendation tries to recommend a sequence of point of interests (POIs) for a user. Most of existing studies search POIs only according to the popularity of POIs themselves. In fact, the routes among the POIs also have attractions to visitors, and some of these routes have high popularity. We term this kind of route as Attractive Route (AR), which brings extra user experience. In this paper, we study the attractive routes to improve personalized trip recommendation. To deal with the challenges of discovery and evaluation of ARs, we propose a personalized Trip Recommender with POIs and Attractive Route (TRAR). It discovers the attractive routes based on the popularity and the Gini coefficient of POIs, then it utilizes a gravity model in a category space to estimate the rating scores and preferences of the attractive routes. Based on that, TRAR recommends a trip with ARs to maximize user experience and leverage the tradeoff between the time cost and the user experience. The experimental results show the superiority of TRAR compared with other state-of-the-art methods.
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DELGADO, JOAQUIN, and NAOHIRO ISHII. "MULTI-AGENT LEARNING IN RECOMMENDER SYSTEMS FOR INFORMATION FILTERING ON THE INTERNET." International Journal of Cooperative Information Systems 10, no. 01n02 (March 2001): 81–100. http://dx.doi.org/10.1142/s0218843001000266.
Повний текст джерелаАнотація:
Recommender Systems (RS), allow users to share information about items they like or dislike and obtain, in a timely fashion, recommendations based on predictions about unseen items (physical or information goods and/or services). In this process, users' preferences are considered to be the learning target functions. We study Agent-based Recommender Systems (ARS) under the scope of online learning in Multi-Agent systems (MAS). This approach models the problem as a pool of independent cooperative predictor agents, one per each user (the masters) in the system, in situations in which each agent (the learners) faces a sequence of trials, with a prediction to make in every step, eventually getting the correct value from its master. Each learner is willing to discover the degree of similarity among the target function of its master and those of other agents' masters (i.e. preference similarity). The agent uses this information for the calculation of its own prediction task, the goal being to make as few mistakes as possible. A simple, yet effective method is introduced in order to construct a compound algorithm for each agent by combining memory-based individual prediction and online weighted-majority voting. We give a theoretical mistake bound for this algorithm that is closely related to the total loss of the best predictor agent in the pool. Finally, we conduct some experiments obtaining results that empirically support these ideas and theories.
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Wu, Shiwen, Yuanxing Zhang, Chengliang Gao, Kaigui Bian, and Bin Cui. "GARG: Anonymous Recommendation of Point-of-Interest in Mobile Networks by Graph Convolution Network." Data Science and Engineering 5, no. 4 (July 29, 2020): 433–47. http://dx.doi.org/10.1007/s41019-020-00135-z.
Повний текст джерелаАнотація:
Abstract The advances of mobile equipment and localization techniques put forward the accuracy of the location-based service (LBS) in mobile networks. One core issue for the industry to exploit the economic interest of the LBSs is to make appropriate point-of-interest (POI) recommendation based on users’ interests. Today, the LBS applications expect the recommender systems to recommend the accurate next POI in an anonymous manner, without inquiring users’ attributes or knowing the detailed features of the vast number of POIs. To cope with the challenge, we propose a novel attentive model to recommend appropriate new POIs for users, namely Geographical Attentive Recommendation via Graph (GARG), which takes full advantage of the collaborative, sequential and content-aware information. Unlike previous strategies that equally treat POIs in the sequence or manually define the relationships between POIs, GARG adaptively differentiates the relevance of POIs in the sequence to the prediction, and automatically identifies the POI-wise correlation. Extensive experiments on three real-world datasets demonstrate the effectiveness of GARG and reveal a significant improvement by GARG on the precision, recall and mAP metrics, compared to several state-of-the-art baseline methods.
Дисертації з теми "Sequence-based recommender"
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MONTI, DIEGO MICHELE. "Multicriteria Evaluation for Top-k and Sequence-based Recommender Systems." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2841172.
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Godard, Pierre. "RNN-based sequence prediction as an alternative or complement to traditional recommender systems." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-216584.
Повний текст джерелаАнотація:
The recurrent neural networks have the ability to grasp the temporal patterns withinthe data. This is a property that can be used in order to help a recommender system bettertaking into account the user past history. Still the dimensionality problem that raiseswithin the recommender system field also raises here as the number of items the systemhave to be aware of is susceptibility high. Recent research have studied the use of such neural networks at a user’s session level.This thesis rather examines the use of this technique at a whole user’s past history levelassociated with techniques such as embeddings and softmax sampling in order to accommodatewith the high dimensionality. The proposed method results in a sequence prediction model that can be used as is forthe recommender task or as a feature within a more complex system.De Recurrent Neural Networks har möjlighet att förstå de tidsmässiga mönstren inom data. Det här är en egenskap som kan användas för att hjälpa ett rekommendatörsystem bättre med hänsyn till användarens historia. Problemet med dimensioner inom rekommendatörsystem uppstår dock även här, eftersom antalet saker som systemet måste vara medveten om är extremt många. Nyare forskning har studerat användningen av sådana neurala nätverk på en användaressessionsnivå. Denna avhandling undersöker snarare användningen av denna teknik som en hel användares tidigare historiknivå i samband med tekniker som inbäddning och softmax-provtagning för att tillgodose den höga dimensionen. Den föreslagna metoden resulterar i en sekvensprediktionsmodell som kan användas som för recommender-uppgiften eller som en funktion inom ett mer komplext system.
Книги з теми "Sequence-based recommender"
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Servin, Frédérique S., and Valérie Billard. Anaesthesia for the obese patient. Edited by Philip M. Hopkins. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0087.
Повний текст джерелаАнотація:
Obesity is becoming an epidemic health problem, and the number of surgical patients with a body mass index of more than 50 kg m−2 requiring anaesthesia is increasing. Obesity is associated with physiopathological changes such as metabolic syndrome, cardiovascular disorders, or sleep apnoea syndrome, most of which improve with weight loss. Regarding pharmacokinetics, volumes of distribution are increased for both lipophilic and hydrophilic drugs. Consequently, doses should be adjusted to total body weight (propofol for maintenance, succinylcholine, vancomycin), or lean body mass (remifentanil, non-depolarizing neuromuscular blocking agent). For all drugs, titration based on monitoring of effects is recommended. To minimize recovery delays, drugs with a rapid offset of action such as remifentanil and desflurane are preferable. Poor tolerance to apnoea with early hypoxaemia and atelectasis warrant rapid sequence induction and protective ventilation. Careful positioning will prevent pressure injuries and minimize rhabdomyolysis which are frequent. Because of an increased risk of pulmonary embolism, multimodal prevention is mandatory. Regional anaesthesia, albeit technically difficult, is beneficial in obese patients to treat postoperative pain and improve rehabilitation. Maximizing the safety of anaesthesia for morbidly obese patients requires a good knowledge of the physiopathology of obesity and great attention to detail in planning and executing anaesthetic management. Even in elective surgery, many cases can be technical challenges and only a step-by-step approach to the avoidance of potential adverse events will result in the optimal outcome.
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Ірина Дмитрівна, Садов’як. CLINICAL MANAGEMENT OF PATIENTS WITH COVID-19. “LIVE” CLINICAL INSTRUCTION (2021). ДЕРЖАВНА НАУКОВА УСТАНОВА «НАУКОВО-ПРАКТИЧНИЙ ЦЕНТР ПРОФІЛАКТИЧНОЇ І КЛІНІЧНОЇ МЕДИЦИНИ», 2021. http://dx.doi.org/10.31612/covid.
Повний текст джерелаАнотація:
SUMMARY. In response to the challenges posed by the coronavirus (COVID-19) pandemic, Ukraine has undergone the necessary legislative changes, harmonized with international approaches, which in turn have led to significant changes in health care practices. The Law of Ukraine “On Amendments to Some Legislative Acts of Ukraine on Provision of Treatment of Coronavirus Disease (COVID-19)” № 539-IX, the Order of the Ministry of Health “On Approval of the Procedure for Prescribing and Using Medicines for the Treatment of Coronavirus Disease (COVID-19)” of 30.06.2020 № 1482, registered in the Ministry of Justice of Ukraine on July 08, 2020 for № 641/34924, establish the conditions of use of registered medicines according to the indications not specified in the instructions for medical use (off label), and unregistered medicines, recommended by the relevant official bodies outside Ukraine for the treatment of COVID-19. In pursuance of legislative acts, the Standard of Emergency Care “Coronavirus Disease (COVID-19)”, the Standards of Medical Care “Coronavirus Disease (COVID-19)”, the Standard of Pharmaceutical Care “Coronavirus Disease (COVID-19)”, the Protocol “Provision of medical care for the treatment of coronavirus disease (COVID-19)” were developed, approved and updated in accordance with the established procedure. At the same time, in order to assist the doctor and the patient in making a rational decision in different clinical situations, a clinical guideline “CLINICAL MANAGEMENT OF PATIENTS WITH COVID-19. “LIVE” CLINICAL INSTRUCTION” was developed – a document containing systematic provisions on medical and medico-social assistance, developed using the methodology of evidence-based medicine on the basis of reliability and proof confirmation. The basis of this clinical guideline is the WHO guideline “Clinical management of COVID-19: interim guidance” (27.05.2020), supplemented by the provisions of other WHO documents, as well as clinical guidelines of Great Britain, Belgium, USA and Australia. This guideline, as a living guideline, is a WHO innovation driven by the urgent need for global collaboration to provide reliable data and guidance emerging in the world as the result of numerous randomized clinical trials on COVID-19. The clinical guideline reflects the sequence of evidence on COVID-19 treatment in the world during a pandemic, on the basis of which the treatment strategy depending on the stage of the disease was formed and the decisions to include and exclude drugs in the protocol for COVID-19 treatment were justified, and will be further updated.
Частини книг з теми "Sequence-based recommender"
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Wang, Ren, and Osmar R. Zaïane. "Sequence-Based Approaches to Course Recommender Systems." In Lecture Notes in Computer Science, 35–50. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98809-2_3.
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Wong, Chris. "Sequence Based Course Recommender for Personalized Curriculum Planning." In Lecture Notes in Computer Science, 531–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93846-2_100.
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Zhou, Yifei, and Conor Hayes. "Graph-Based Diffusion Method for Top-N Recommendation." In Communications in Computer and Information Science, 292–304. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_23.
Повний текст джерелаАнотація:
AbstractData that may be used for personalised recommendation purposes can intuitively be modelled as a graph. Users can be linked to item data; item data may be linked to item data. With such a model, the task of recommending new items to users or making new connections between items can be undertaken by algorithms designed to establish the relatedness between vertices in a graph. One such class of algorithm is based on the random walk, whereby a sequence of connected vertices are visited based on an underlying probability distribution and a determination of vertex relatedness established. A diffusion kernel encodes such a process. This paper demonstrates several diffusion kernel approaches on a graph composed of user-item and item-item relationships. The approach presented in this paper, RecWalk*, consists of a user-item bipartite combined with an item-item graph on which several diffusion kernels are applied and evaluated in terms of top-n recommendation. We conduct experiments on several datasets of the RecWalk* model using combinations of different item-item graph models and personalised diffusion kernels. We compare accuracy with some non-item recommender methods. We show that diffusion kernel approaches match or outperform state-of-the-art recommender approaches.
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Zhou, Mingming, and Yabo Xu. "Challenges to Use Recommender Systems to Enhance Meta-Cognitive Functioning in Online Learners." In Educational Recommender Systems and Technologies, 282–301. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-61350-489-5.ch012.
Повний текст джерелаАнотація:
A wealth of research has shown that meta-cognition plays a crucial role in the promotion of effective school learning. In most of the e-learning environment designs, however, meta-cognitive strategies have generally been neglected, and therefore, satisfactory uses of these strategies have rarely been realized. Most learners are not even aware of what they have been studying. If the learning system could automatically guide and intelligently recommend learning activities or strategies to facilitate student monitoring and control of their learning, it would favor and improve their learning process and performance. Unfortunately, nearly no e-learning systems to date have attempted to do so. In this chapter, we first described the need for enhancing meta-cognitive skills in e-learning environment, followed by an outline of major challenges for meta-cognitive activity recommendations. We then proposed to adopt data mining algorithms (i.e., content-based and sequence-based recommendation techniques) to meet the identified issues with a toy example.
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Zhou, Mingming, and Yabo Xu. "Challenges to Use Recommender Systems to Enhance Meta-Cognitive Functioning in Online Learners." In Data Mining, 1916–35. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2455-9.ch099.
Повний текст джерелаАнотація:
A wealth of research has shown that meta-cognition plays a crucial role in the promotion of effective school learning. In most of the e-learning environment designs, however, meta-cognitive strategies have generally been neglected, and therefore, satisfactory uses of these strategies have rarely been realized. Most learners are not even aware of what they have been studying. If the learning system could automatically guide and intelligently recommend learning activities or strategies to facilitate student monitoring and control of their learning, it would favor and improve their learning process and performance. Unfortunately, nearly no e-learning systems to date have attempted to do so. In this chapter, we first described the need for enhancing meta-cognitive skills in e-learning environment, followed by an outline of major challenges for meta-cognitive activity recommendations. We then proposed to adopt data mining algorithms (i.e., content-based and sequence-based recommendation techniques) to meet the identified issues with a toy example.
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Radwan, Nouran M., and Wael K. Hanna. "An Adaptive eLearning Sequence Based on Neutrosophic Logic." In Handbook of Research on Advances and Applications of Fuzzy Sets and Logic, 619–38. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7979-4.ch028.
Повний текст джерелаАнотація:
In recent years, e-learning has become a revolutionary competitive method. Adapting the content according to learner knowledge is a current challenge in e-learning systems. Currently, most of the e-learning systems evaluate the learner's knowledge level according to crisp responses that are taken during the learning process. Therefore, one of the most significant challenges in e-learning is how to improve the course adaptation in order to achieve high-quality interaction for all learners. Adaptation is an efficient way to help learners to learn their learning activities in easy and a suitable ways. However, there are many factors that lead to uncertainty about the learner evaluation process. This chapter presents a novel approach to handle imprecision, vagueness, ambiguity, and inconsistency in the learner evaluation process to recommend the suitable learning material according to the learner's knowledge level.
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Golo Barro, Seydou, Adrien Ugon, Nadège R. Nana, and Pascal Staccini. "Design and Implementation of a Unique Patient Identification Model in Information Systems in Burkina Faso." In MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation. IOS Press, 2022. http://dx.doi.org/10.3233/shti220070.
Повний текст джерелаАнотація:
The implementation of a reliable identity process is the basis of any secure patient information sharing system. Indeed, each individual is unique and should be identified by a unique number (identifier). It is with these issues in mind that we have designed and implemented a unique patient identification method adapted to the context of Burkina Faso. The recommended method is inspired by the French method based on the work of the Group for the Modernization of the Hospital Information System (GMSIH) [1]. The developed model allows to assign a “Unique Identifier” (PatientID) to each patient from his profile of identification features (name, date of birth, gender,…). The patient ID is a sequence of 20 characters plus a security “key” of 2 characters. A reliability test of the model has been performed to take into account identity anomalies (duplicate, collision).
Тези доповідей конференцій з теми "Sequence-based recommender"
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Demir, Gül Nildem, A. Sima Uyar, and Sule Gündüz Ögüdücü. "Graph-based sequence clustering through multiobjective evolutionary algorithms for web recommender systems." In the 9th annual conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1276958.1277346.
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Gurbanov, Tural, and Francesco Ricci. "Action prediction models for recommender systems based on collaborative filtering and sequence mining hybridization." In SAC 2017: Symposium on Applied Computing. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3019612.3019759.
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Li, Yang, Tong Chen, Yadan Luo, Hongzhi Yin, and Zi Huang. "Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/206.
Повний текст джерелаАнотація:
Being an indispensable component in location-based social networks, next point-of-interest (POI) recommendation recommends users unexplored POIs based on their recent visiting histories. However, existing work mainly models check-in data as isolated POI sequences, neglecting the crucial collaborative signals from cross-sequence check-in information. Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation. In this paper, we propose Sequence-to-Graph (Seq2Graph) augmentation for each POI sequence, allowing collaborative signals to be propagated from correlated POIs belonging to other sequences. We then devise a novel Sequence-to-Graph POI Recommender (SGRec), which jointly learns POI embeddings and infers a user's temporal preferences from the graph-augmented POI sequence. To overcome the sparsity of POI-level interactions, we further infuse category-awareness into SGRec with a multi-task learning scheme that captures the denser category-wise transitions. As such, SGRec makes full use of the collaborative signals for learning expressive POI representations, and also comprehensively uncovers multi-level sequential patterns for user preference modelling. Extensive experiments on two real-world datasets demonstrate the superiority of SGRec against state-of-the-art methods in next POI recommendation.
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Hu, Liang, Longbing Cao, Shoujin Wang, Guandong Xu, Jian Cao, and Zhiping Gu. "Diversifying Personalized Recommendation with User-session Context." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/258.
Повний текст джерелаАнотація:
Recommender systems (RS) have become an integral part of our daily life. However, most current RS often repeatedly recommend items to users with similar profiles. We argue that recommendation should be diversified by leveraging session contexts with personalized user profiles. For this, current session-based RS (SBRS) often assume a rigidly ordered sequence over data which does not fit in many real-world cases. Moreover, personalization is often omitted in current SBRS. Accordingly, a personalized SBRS over relaxedly ordered user-session contexts is more pragmatic. In doing so, deep-structured models tend to be too complex to serve for online SBRS owing to the large number of users and items. Therefore, we design an efficient SBRS with shallow wide-in-wide-out networks, inspired by the successful experience in modern language modelings. The experiments on a real-world e-commerce dataset show the superiority of our model over the state-of-the-art methods.
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Li, Muyang, Xiangyu Zhao, Chuan Lyu, Minghao Zhao, Runze Wu, and Ruocheng Guo. "MLP4Rec: A Pure MLP Architecture for Sequential Recommendations." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/297.
Повний текст джерелаАнотація:
Self-attention models have achieved state-of-the-art performance in sequential recommender systems by capturing the sequential dependencies among user-item interactions. However, they rely on positional embeddings to retain the sequential information, which may break the semantics of item embeddings. In addition, most existing works assume that such sequential dependencies exist solely in the item embeddings, but neglect their existence among the item features. In this work, we propose a novel sequential recommender system (MLP4Rec) based on the recent advances of MLP-based architectures, which is naturally sensitive to the order of items in a sequence. To be specific, we develop a tri-directional fusion scheme to coherently capture sequential, cross-channel and cross-feature correlations. Extensive experiments demonstrate the effectiveness of MLP4Rec over various representative baselines upon two benchmark datasets. The simple architecture of MLP4Rec also leads to the linear computational complexity as well as much fewer model parameters than existing self-attention methods.
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Cai, Chenwei, Ruining He, and Julian McAuley. "SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/204.
Повний текст джерелаАнотація:
Dealing with sparse, long-tailed datasets, and cold-start problems is always a challenge for recommender systems. These issues can partly be dealt with by making predictions not in isolation, but by leveraging information from related events; such information could include signals from social relationships or from the sequence of recent activities. Both types of additional information can be used to improve the performance of state-of-the-art matrix factorization-based techniques. In this paper, we propose new methods to combine both social and sequential information simultaneously, in order to further improve recommendation performance. We show these techniques to be particularly effective when dealing with sparsity and cold-start issues in several large, real-world datasets.
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Huang, Xiaoping, Xiaoshun Yan, Muk Chen Ong, and Yingcai Huang. "The Effect of Fatigue Loading Spectrum on Crack Propagation in a Ship Detail." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77152.
Повний текст джерелаАнотація:
The assessment of the loading sequence on fatigue crack growth of cracked details is essential when the ship hull is subjected to random wave-induced loading during the service time. In this paper, the effect of loading sequence on the crack growth life is investigated in order to find a physical engineering method to generate fatigue loading spectrum for ship fatigue assessment. The long term distribution of fatigue loading and the unique crack growth rate curve model have been employed in the analysis. The results show that the shape parameter affects the fatigue life significantly if the long-term distribution of wave-induced stress range is modeled by two-parameter Weibull distribution. Moreover, the spectral-based method provides a reasonable fatigue loading spectrum and avoid the confusion in determining the shape parameter for different empirical formulas, which are recommended by several main ship classification societies. An example of fatigue assessment for a cracked detail in a container ship is demonstrated as a reference for fatigue assessment of a ship hull based on crack growth.
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Diemunsch, Kenneth. "CBTC Field Test and Commissioning." In 2015 Joint Rail Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/jrc2015-5614.
Повний текст джерелаАнотація:
Communications-Based Train Control (CBTC) technology is the most advanced train control system for urban railway infrastructures. It is very different from conventional relay based signaling systems and more complex than most cab signaling systems. CBTC functions are numerous, highly complex with customized details for each project. They cannot be tested for all possible conditions at all locations. Knowledge of the CTBC system and experience with train control commissioning are keys to performing enough tests to detect most issues but permit the start of revenue service as early as possible. The testing strategy proposed by the CBTC supplier is the result of years of experience with the goal of minimizing expensive field tests while demonstrating that the system will work properly in revenue service. Despite the numerous tests performed before revenue service, it is inevitable that operating challenges will be faced during the first months of CBTC system operation. The recent Institute of Electrical and Electronics Engineers (IEEE) Std 1474.4-2011 Recommended Practice for Functional Testing of a Communications-Based Train Control System [1] provides a good description how and where CBTC functions should be tested. However, it does not describe the sequence of tests in the context of a project where CBTC is deployed on a transit property. This paper presents the sequence of field tests required to commission a CBTC system and provides insight based on experience with several CBTC projects in the last decade.
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Di Blasi, Marti´n, Gustavo Felici, Walter Ramponi, and Juan Czarnowski. "Design of Contingency Plans for Pipeline Leakage Using Hydraulic Simulation." In 2006 International Pipeline Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/ipc2006-10214.
Повний текст джерелаАнотація:
The use of leak detection and location systems has became a common practice in the control rooms of pipeline operations, whereby operators are automatically alerted of a leak, and informed of its approximate location. Considerable attention has primarily been paid to leak sensitivity and leak detection time. However, this paper shows that in long pipelines the volume lost after a pipeline shutdown can be substantially more than what was lost prior to the detection. In very irregular altimetry, the drainage from the pipeline segment can represent a greater volume of spillage as compared to the spillage from the start of the leak until its detection. These volumes can be reduced by taking the appropriate actions. This work will illustrate the importance of having a leak contingency plan that orients the operators to take suitable actions that will diminish the volume spilled. A leak contingency plan is a detailed plan of recommended actions, for example valve closure and pumping shutdown, and the specific time sequence in which they have to be executed. The design of these plans was based on the use of hydraulic transient simulations of an actual 3000-km network in Argentina.
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Silva, Kampanart, Yuki Ishiwatari, and Shogo Takahara. "Integration of Direct/Indirect Influences of Severe Accidents for Improvements of Nuclear Safety." In 2012 20th International Conference on Nuclear Engineering and the ASME 2012 Power Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/icone20-power2012-55002.
Повний текст джерелаАнотація:
Risk evaluation is an important assessment tool of nuclear safety, and a common index of direct/indirect influences of severe accidents as a compound of risk is necessary then. In this research, various influences of severe accidents are converted to monetary value and integrated. The integrated influence is calculated in a unit of “cost per severe accident” and “cost per kWh”. The authors must emphasize that the aim is not to estimate the accident cost itself but to extend the scope of “risk-informed decision making” for continuous safety improvements of nuclear energy. To calculate the “cost per severe accident” and the “cost per kWh”, typical sequences of severe accidents are picked-up first. Containment failure frequency (CFF) and source terms of each sequence are taken from the results of level 2 probabilistic risk assessment (PRA). The source terms of each sequence is input into the level 3 PRA code OSCAAR which was developed by Japan Atomic Energy Agency (JAEA). The calculations have been made for 248 meteorological sequences, and the results presented in this study are given as expectation values for various meteorological conditions. Using these outputs, the cost per severe accident is calculated. It consists of various costs and other influences converted into monetary values. This methodology is applied to a virtual 1,100 MWe BWR-5 plant. Seismic events are considered as the initiating events. The data obtained from the open documents on the Fukushima Accident are utilized as much as possible. Sensitivity analyses are carried out to identify the dominant influences, sensitive assumptions/parameters to the cost per accident or per kWh. Based on these findings, optimization of radiation protection countermeasures is recommended. Also, the effects of sever accident management are investigated.
Звіти організацій з теми "Sequence-based recommender"
1
Joel, Daniel M., Steven J. Knapp, and Yaakov Tadmor. Genomic Approaches for Understanding Virulence and Resistance in the Sunflower-Orobanche Host-Parasite Interaction. United States Department of Agriculture, August 2011. http://dx.doi.org/10.32747/2011.7592655.bard.
Повний текст джерелаАнотація:
Oroginal Objectives: (i) identify DNA markers linked to the avirulence (Avr) locus and locate the Avr locus through genetic mapping with an inter-race Orobanche cumana population; (ii) develop high-throughput fingerprint DNA markers for genotypingO. cumana races; (iii) identify nucleotide binding domain leucine rich repeat (NB-LRR) genes encoding R proteins conferring resistance to O. cumana in sunflower; (iv) increase the resolution of the chromosomal segment harboring Or₅ and related R genes through genetic and physical mapping in previously and newly developed mapping populations of sunflower; and (v) develop high-throughput DNA markers for rapidly and efficiently identifying and transferring sunflower R genes through marker-assisted selection. Revisions made during the course of project: Following changes in O. cumana race distribution in Israel, the newly arrived virulent race H was chosen for further analysis. HA412-HO, which was primarily chosen as a susceptible sunflower cultivar, was more resistant to the new parasite populations than var. Shemesh, thus we shifted sunflower research into analyzing the resistance of HA412-HO. We exceeded the deliverables for Objectives #3-5 by securing funding for complete physical and high-density genetic mapping of the sunflower genome, in addition to producing a complete draft sequence of the sunflower genome. We discovered limited diversity between the parents of the O. cumana population developed for the mapping study. Hence, the developed DNA marker resources were insufficient to support genetic map construction. This objective was beyond the scale and scope of the funding. This objective is challenging enough to be the entire focus of follow up studies. Background to the topic: O. cumana, an obligate parasitic weed, is one of the most economically important and damaging diseases of sunflower, causes significant yield losses in susceptible genotypes, and threatens production in Israel and many other countries. Breeding for resistance has been crucial for protecting sunflower from O. cumana, and problematic because new races of the pathogen continually emerge, necessitating discovery and deployment of new R genes. The process is challenging because of the uncertainty in identifying races in a genetically diverse parasite. Major conclusions, solutions, achievements: We developed a small collection of SSR markers for genetic mapping in O. cumana and completed a diversity study to lay the ground for objective #1. Because DNA sequencing and SNPgenotyping technology dramatically advanced during the course of the study, we recommend shifting future work to SNP discovery and mapping using array-based approaches, instead of SSR markers. We completed a pilot study using a 96-SNP array, but it was not large enough to support genetic mapping in O.cumana. The development of further SNPs was beyond the scope of the grant. However, the collection of SSR markers was ideal for genetic diversity analysis, which indicated that O. cumanapopulations in Israel considerably differ frompopulations in other Mediterranean countries. We supplied physical and genetic mapping resources for identifying R-genes in sunflower responsible for resistance to O. cumana. Several thousand mapped SNP markers and a complete draft of the sunflower genome sequence are powerful tools for identifying additional candidate genes and understanding the genomic architecture of O. cumana-resistanceanddisease-resistance genes. Implications: The OrobancheSSR markers have utility in sunflower breeding and genetics programs, as well as a tool for understanding the heterogeneity of races in the field and for geographically mapping of pathotypes.The segregating populations of both Orobanche and sunflower hybrids are now available for QTL analyses.
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Fahima, Tzion, and Jorge Dubcovsky. Map-based cloning of the novel stripe rust resistance gene YrG303 and its use to engineer 1B chromosome with multiple beneficial traits. United States Department of Agriculture, January 2013. http://dx.doi.org/10.32747/2013.7598147.bard.
Повний текст джерелаАнотація:
Research problem: Bread wheat (Triticumaestivum) provides approximately 20% of the calories and proteins consumed by humankind. As the world population continues to increase, it is necessary to improve wheat yields, increase grain quality, and minimize the losses produced by biotic and abiotic stresses. Stripe rust, caused by Pucciniastriiformisf. sp. tritici(Pst), is one of the most destructive diseases of wheat. The new pathogen races are more virulent and aggressive than previous ones and have produced large economic losses. A rich source for stripe-rust resistance genes (Yr) was found in wild emmer wheat populations from Israel. Original Project goals: Our long term goal is to identify, map, clone, characterize and deploy in breeding, novel wild emmer Yr genes, and combine them with multiple beneficial traits. The current study was aiming to map and clone YrG303 and Yr15, located on chromosome 1BS and combine them with drought resistance and grain quality genes. Positional cloning of YrG303/Yr15: Fine mapping of these genes revealed that YrG303 is actually allelic to Yr15. Fine genetic mapping using large segregating populations resulted in reduction of the genetic interval spanning Yr15 to less than 0.1 cM. Physical mapping of the YrG303/Yr15 locus was based on the complete chromosome 1BS physical map of wheat constructed by our group. Screening of 1BS BAC library with Yr15 markers revealed a long BAC scaffold covering the target region. The screening of T. dicoccoidesaccession-specific BAC library with Yr15 markers resulted in direct landing on the target site. Sequencing of T. dicoccoidesBAC clones that cover the YrG303/Yr15 locus revealed a single candidate gene (CG) with conserved domains that may indicate a role in disease resistance response. Validation of the CG was carried out using EMS mutagenesis (loss-of- function approach). Sequencing of the CG in susceptible yr15/yrG303 plants revealed three independent mutants that harbour non-functional yr15/yrG303 alleles within the CG conserved domains, and therefore validated its function as a Pstresistance gene. Evaluation of marker-assisted-selection (MAS) for Yr15. Introgressions of Yr15 into cultivated wheat are widely used now. Recently, we have shown that DNA markers linked to Yr15 can be used as efficient tools for introgression of Yr15 into cultivated wheat via MAS. The developed markers were consistent and polymorphic in all 34 tested introgressions and are the most recommended markers for the introgression of Yr15. These markers will facilitate simultaneous selection for multiple Yr genes and help to avoid escapees during the selection process. Engineering of improved chromosome 1BS that harbors multiple beneficial traits. We have implemented the knowledge and genetic resources accumulated in this project for the engineering of 1B "super-chromosome" that harbors multiple beneficial traits. We completed the generation of a chromosome including the rye 1RS distal segment associated with improved drought tolerance with the Yr gene, Yr15, and the strong gluten allele 7Bx-over-expressor (7Bxᴼᴱ). We have completed the introgression of this improved chromosome into our recently released variety Patwin-515HP and our rain fed variety Kern, as well as to our top breeding lines UC1767 and UC1745. Elucidating the mechanism of resistance exhibited by Yr36 (WKS1). The WHEAT KINASE START1 (WKS1) resistance gene (Yr36) confers partial resistance to Pst. We have shown that wheat plants transformed with WKS1 transcript are resistant to Pst. WKS1 is targeted to the chloroplast where it phosphorylates the thylakoid-associatedascorbateperoxidase (tAPX) and reduces its ability to detoxify peroxides. Based on these results, we propose that the phosphorylation of tAPX by WKS1 reduces the ability of the cells to detoxify ROS and contributes to cell death. Distribution and diversity of WKS in wild emmer populations. We have shown that WKS1 is present only in the southern distribution range of wild emmer in the Fertile Crescent. Sequence analysis revealed a high level of WKS1 conservation among wild emmer populations, in contrast to the high level of diversity observed in NB-LRR genes. This phenomenon shed some light on the evolution of genes that confer partial resistance to Pst. Three new WKS1 haplotypes displayed a resistance response, suggesting that they can be useful to improve wheat resistance to Pst. In summary, we have improved our understanding of cereals’ resistance mechanisms to rusts and we have used that knowledge to develop improved wheat varieties.
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Jorgensen, Frieda, Andre Charlett, Craig Swift, Anais Painset, and Nicolae Corcionivoschi. A survey of the levels of Campylobacter spp. contamination and prevalence of selected antimicrobial resistance determinants in fresh whole UK-produced chilled chickens at retail sale (non-major retailers). Food Standards Agency, June 2021. http://dx.doi.org/10.46756/sci.fsa.xls618.
Повний текст джерелаАнотація:
Campylobacter spp. are the most common bacterial cause of foodborne illness in the UK, with chicken considered to be the most important vehicle for this organism. The UK Food Standards Agency (FSA) agreed with industry to reduce Campylobacter spp. contamination in raw chicken and issued a target to reduce the prevalence of the most contaminated chickens (those with more than 1000 cfu per g chicken neck skin) to below 10 % at the end of the slaughter process, initially by 2016. To help monitor progress, a series of UK-wide surveys were undertaken to determine the levels of Campylobacter spp. on whole UK-produced, fresh chicken at retail sale in the UK. The data obtained for the first four years was reported in FSA projects FS241044 (2014/15) and FS102121 (2015 to 2018). The FSA has indicated that the retail proxy target for the percentage of highly contaminated raw whole retail chickens should be less than 7% and while continued monitoring has demonstrated a sustained decline for chickens from major retailer stores, chicken on sale in other stores have yet to meet this target. This report presents results from testing chickens from non-major retailer stores (only) in a fifth survey year from 2018 to 2019. In line with previous practise, samples were collected from stores distributed throughout the UK (in proportion to the population size of each country). Testing was performed by two laboratories - a Public Health England (PHE) laboratory or the Agri-Food & Biosciences Institute (AFBI), Belfast. Enumeration of Campylobacter spp. was performed using the ISO 10272-2 standard enumeration method applied with a detection limit of 10 colony forming units (cfu) per gram (g) of neck skin. Antimicrobial resistance (AMR) to selected antimicrobials in accordance with those advised in the EU harmonised monitoring protocol was predicted from genome sequence data in Campylobacter jejuni and Campylobacter coli isolates The percentage (10.8%) of fresh, whole chicken at retail sale in stores of smaller chains (for example, Iceland, McColl’s, Budgens, Nisa, Costcutter, One Stop), independents and butchers (collectively referred to as non-major retailer stores in this report) in the UK that are highly contaminated (at more than 1000 cfu per g) with Campylobacter spp. has decreased since the previous survey year but is still higher than that found in samples from major retailers. 8 whole fresh raw chickens from non-major retailer stores were collected from August 2018 to July 2019 (n = 1009). Campylobacter spp. were detected in 55.8% of the chicken skin samples obtained from non-major retailer shops, and 10.8% of the samples had counts above 1000 cfu per g chicken skin. Comparison among production plant approval codes showed significant differences of the percentages of chicken samples with more than 1000 cfu per g, ranging from 0% to 28.1%. The percentage of samples with more than 1000 cfu of Campylobacter spp. per g was significantly higher in the period May, June and July than in the period November to April. The percentage of highly contaminated samples was significantly higher for samples taken from larger compared to smaller chickens. There was no statistical difference in the percentage of highly contaminated samples between those obtained from chicken reared with access to range (for example, free-range and organic birds) and those reared under standard regime (for example, no access to range) but the small sample size for organic and to a lesser extent free-range chickens, may have limited the ability to detect important differences should they exist. Campylobacter species was determined for isolates from 93.4% of the positive samples. C. jejuni was isolated from the majority (72.6%) of samples while C. coli was identified in 22.1% of samples. A combination of both species was found in 5.3% of samples. C. coli was more frequently isolated from samples obtained from chicken reared with access to range in comparison to those reared as standard birds. C. jejuni was less prevalent during the summer months of June, July and August compared to the remaining months of the year. Resistance to ciprofloxacin (fluoroquinolone), erythromycin (macrolide), tetracycline, (tetracyclines), gentamicin and streptomycin (aminoglycosides) was predicted from WGS data by the detection of known antimicrobial resistance determinants. Resistance to ciprofloxacin was detected in 185 (51.7%) isolates of C. jejuni and 49 (42.1%) isolates of C. coli; while 220 (61.1%) isolates of C. jejuni and 73 (62.9%) isolates of C. coli isolates were resistant to tetracycline. Three C. coli (2.6%) but none of the C. jejuni isolates harboured 23S mutations predicting reduced susceptibility to erythromycin. Multidrug resistance (MDR), defined as harbouring genetic determinants for resistance to at least three unrelated antimicrobial classes, was found in 10 (8.6%) C. coli isolates but not in any C. jejuni isolates. Co-resistance to ciprofloxacin and erythromycin was predicted in 1.7% of C. coli isolates. 9 Overall, the percentages of isolates with genetic AMR determinants found in this study were similar to those reported in the previous survey year (August 2016 to July 2017) where testing was based on phenotypic break-point testing. Multi-drug resistance was similar to that found in the previous survey years. It is recommended that trends in AMR in Campylobacter spp. isolates from retail chickens continue to be monitored to realise any increasing resistance of concern, particulary to erythromycin (macrolide). Considering that the percentage of fresh, whole chicken from non-major retailer stores in the UK that are highly contaminated (at more than 1000 cfu per g) with Campylobacter spp. continues to be above that in samples from major retailers more action including consideration of interventions such as improved biosecurity and slaughterhouse measures is needed to achieve better control of Campylobacter spp. for this section of the industry. The FSA has indicated that the retail proxy target for the percentage of highly contaminated retail chickens should be less than 7% and while continued monitoring has demonstrated a sustained decline for chickens from major retailer stores, chicken on sale in other stores have yet to meet this target.