Academic literature on the topic 'COMmunity interest based RECommendation system'
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Journal articles on the topic "COMmunity interest based RECommendation system"
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Zhang, Hong, Dechu Ge, and Siyu Zhang. "Hybrid recommendation system based on semantic interest community and trusted neighbors." Multimedia Tools and Applications 77, no. 4 (March 20, 2017): 4187–202. http://dx.doi.org/10.1007/s11042-017-4553-9.
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Zheng, Jianxing, Suge Wang, Deyu Li, and Bofeng Zhang. "Personalized recommendation based on hierarchical interest overlapping community." Information Sciences 479 (April 2019): 55–75. http://dx.doi.org/10.1016/j.ins.2018.11.054.
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Zheng, Jianxing, and Yanjie Wang. "Personalized Recommendations Based on Sentimental Interest Community Detection." Scientific Programming 2018 (August 5, 2018): 1–14. http://dx.doi.org/10.1155/2018/8503452.
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Communities have become a popular platform of mining interests for recommender systems. The semantics of topics reflect users’ implicit interests. Sentiments on topics imply users’ sentimental tendency. People with common sentiments can form resonant communities of interest. In this paper, a resonant sentimental interest community-based recommendation model is proposed to improve the accuracy performance of recommender systems. First, we learn the weighted semantics vector and sentiment vector to model semantic and sentimental user profiles. Then, by combining semantic and sentimental factors, resonance relationship is computed to evaluate the resonance relationship of users. Finally, based on resonance relationships, resonant community is detected to discover a resonance group to make personalized recommendations. Experimental results show that the proposed model is more effective in finding semantics-related sentimental interests than traditional methods.
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Wenwen, Zhou. "Building an Urban Smart Community System Based on Association Rule Algorithms." Security and Communication Networks 2022 (July 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/8773259.
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Intelligent system development is an integral component of smart community development and has a significant impact on the development of smart communities. Some cities continue to implement personalized smart community services, resulting in the formation of smart city communities with unique characteristics. Urban smart communities are based on the principle of owner-occupant convenience, integrating a wealth of community information and making it more relevant to each and every resident through intelligent management. Increasing information transmission rates have enhanced the ability of smart community systems to integrate information, but the smart community recommendation method is still based on traditional categorized recommendations. This paper addresses the deficiency of recommended information in smart urban communities. By analyzing user interaction and operation data, we can determine the interest and recognition of browsing attractions among users. Compared to conventional classification recommendations, weighted association rules can identify potentially very important rules applicable to small groups, thereby meeting the needs of various groups and enabling personalized services. Through continuous feedback from user behavior data, the system gradually identifies the community information that users are interested in during the specific recommendation process. After testing, the smart community system’s recommendation accuracy and real-time performance have vastly improved in comparison to categorical recommendations, and it can effectively meet the needs of tenants for community recommendations.
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Zhou, Tom, Hao Ma, Michael Lyu, and Irwin King. "UserRec: A User Recommendation Framework in Social Tagging Systems." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1486–91. http://dx.doi.org/10.1609/aaai.v24i1.7524.
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Social tagging systems have emerged as an effective way for users to annotate and share objects on the Web. However, with the growth of social tagging systems, users are easily overwhelmed by the large amount of data and it is very difficult for users to dig out information that he/she is interested in. Though the tagging system has provided interest-based social network features to enable the user to keep track of other users' tagging activities, there is still no automatic and effective way for the user to discover other users with common interests. In this paper, we propose a User Recommendation (UserRec) framework for user interest modeling and interest-based user recommendation, aiming to boost information sharing among users with similar interests. Our work brings three major contributions to the research community: (1) we propose a tag-graph based community detection method to model the users' personal interests, which are further represented by discrete topic distributions; (2) the similarity values between users' topic distributions are measured by Kullback-Leibler divergence (KL-divergence), and the similarity values are further used to perform interest-based user recommendation; and (3) by analyzing users' roles in a tagging system, we find users' roles in a tagging system are similar to Web pages in the Internet. Experiments on tagging dataset of Web pages (Yahoo!~Delicious) show that UserRec outperforms other state-of-the-art recommender system approaches.
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Gan, Mingxin, and Xiongtao Zhang. "Integrating Community Interest and Neighbor Semantic for Microblog Recommendation." International Journal of Web Services Research 18, no. 2 (April 2021): 54–75. http://dx.doi.org/10.4018/ijwsr.2021040104.
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As a typical characteristic of microblog information, short text length makes a microblog recommendation hard for new users. Moreover, user cold start makes it difficult to explore accurately the interests of microblog users. Therefore, the authors proposed a microblog recommendation model that integrates both of the users' interest from their communities and the semantic from their neighbors' microblogs. Based on the Kullback-Leibler (KL) language model, the proposed model estimated an interest-based language model and a microblog-based language model. Specifically, the interest-based language model was estimated based on both of the user's word set of interest and that of their community interest. Meanwhile, the microblog-based language model was estimated by combining the word set of a microblog, the neighbor semantic, and the microblog set. Real data from Sina Weibo was crawled to evaluate recommendation performance. Results showed that the proposed model outperforms state-of-art models significantly.
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Tang, Lei, Dandan Cai, Zongtao Duan, Junchi Ma, Meng Han, and Hanbo Wang. "Discovering Travel Community for POI Recommendation on Location-Based Social Networks." Complexity 2019 (February 12, 2019): 1–8. http://dx.doi.org/10.1155/2019/8503962.
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Point-of-interest (POI) recommendations are a popular form of personalized service in which users share their POI location and related content with their contacts in location-based social networks (LBSNs). The similarity and relatedness between users of the same POI type are frequently used for trajectory retrieval, but most of the existing works rely on the explicit characteristics from all users’ check-in records without considering individual activities. We propose a POI recommendation method that attempts to optimally recommend POI types to serve multiple users. The proposed method aims to predict destination POIs of a user and search for similar users of the same regions of interest, thus optimizing the user acceptance rate for each recommendation. The proposed method also employs the variable-order Markov model to determine the distribution of a user’s POIs based on his or her travel histories in LBSNs. To further enhance the user’s experience, we also apply linear discriminant analysis to cluster the topics related to “Travel” and connect to users with social links or similar interests. The probability of POIs based on users’ historical trip data and interests in the same topics can be calculated. The system then provides a list of the recommended destination POIs ranked by their probabilities. We demonstrate that our work outperforms collaborative-filtering-based and other methods using two real-world datasets from New York City. Experimental results show that the proposed method is better than other models in terms of both accuracy and recall. The proposed POI recommendation algorithms can be deployed in certain online transportation systems and can serve over 100,000 users.
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Shokrzadeh, Zeinab, Mohammad-Reza Feizi-Derakhshi, Mohammad-Ali Balafar, and Jamshid Bagherzadeh Mohasefi. "Graph-Based Recommendation System Enhanced by Community Detection." Scientific Programming 2023 (August 21, 2023): 1–12. http://dx.doi.org/10.1155/2023/5073769.
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Many researchers have used tag information to improve the performance of recommendation techniques in recommender systems. Examining the tags of users will help to get their interests and leads to more accuracy in the recommendations. Since user-defined tags are chosen freely and without any restrictions, problems arise in determining their exact meaning and the similarity of tags. However, using thesaurus and ontologies to find the meaning of tags is not very efficient due to their free definition by users and the use of different languages in many data sets. Therefore, this article uses mathematical and statistical methods to determine lexical similarity and co-occurrence tags solution to assign semantic similarity. On the other hand, due to the change of users’ interests over time this article has considered the time of tag assignments in co-occurrence tags for determining the similarity of tags. Then the graph is created based on similarity of tags. For modeling the interests of the users, the communities of tags are determined by using community detection methods. So, recommendations based on the communities of tags and similarity between resources are done. The performance of the proposed method has been evaluated using two criteria of precision and recall through evaluations on two public datasets. The evaluation results show that the precision and recall of the proposed method have significantly improved, compared to the other methods. According to the experimental results, the criteria of recall and precision have been improved, on average by 5% and 7%, respectively.
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Kumar, Akshi, and Saurabh Raj Sangwan. "Expert Finding in Community Question-Answering for Post Recommendation." International Journal of Engineering & Technology 7, no. 3.4 (June 25, 2018): 151. http://dx.doi.org/10.14419/ijet.v7i3.4.16764.
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Community question answering system is a perfect example of platform where people participate to seek expertise on their topic of interest. But information overload, finding the expertise level of users and trustworthy answers remain key challenges within these communities. Moreover, people do not look for personal advices but expert views on such platforms therefore; expert finding is an integral part of these communities. In order to trust someone's opinion who is not known in person by the users of the community, it is necessary to find the credibility of such person. By determining expertise levels of users, authenticity of their posts can easily be determined. Also, by identifying experts, each expert will be shown relevant posts to indulge in so that he can use his knowledge and skills to give valid and correct answers. For users too, it will be easy to find reliable answers, once they get to know the expertise level of the answerers. Motivated by these facts, we put forward a framework for finding experts in online question answer community (stackoverflow) referred to as Expert Recommender System which uses a well-recognized global-trust metric, PageRankTM for finding experts in the community building a Trust-based system and then uses collaborative filtering to find similar experts based on their level of expertise and their topics of interests to a particular user. Once we have the top- k similar experts to a given expert, that expert is recommended with posts to collaborate upon, based on activities done by his top-k neighbor experts. The framework is evaluated for its performance and it clearly indicates the effectiveness of the system.
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Liu, Jing, and Yong Zhong. "Time-Weighted Community Search Based on Interest." Applied Sciences 12, no. 14 (July 13, 2022): 7077. http://dx.doi.org/10.3390/app12147077.
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Community search aims to provide users with personalized community query services. It is a prerequisite for various recommendation systems and has received widespread attention from academia and industry. The existing literature has established various community search models and algorithms from different dimensions of social networks. Unfortunately, they only judge the representative attributes of users according to the frequency of attribute keywords, completely ignoring the temporal characteristics of keywords. It is clear that a user’s interest changes over time, so it is essential to select users’ representative attributes in combination with time. Therefore, we propose a time-weighted community search model (TWC) based on user interests which fully considers the impact of time on user interests. TWC reduces the number of query parameters as much as possible and improves the usability of the model. We design the time-weighted decay function of the attribute. We then extract the user’s time-weighted representative attributes to express the user’s short-term interests more clearly in the query window. In addition, we propose a new attribute similarity scoring function and a community scoring function. To solve the TWC problem, we design and implement the Local Extend algorithm and the Shrink algorithm. Finally, we conduct extensive experiments on a real dataset to verify the superiority of the TWC model and the efficiency of the proposed algorithm.
Dissertations / Theses on the topic "COMmunity interest based RECommendation system"
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Khater, Shaymaa. "Personalized Recommendation for Online Social Networks Information: Personal Preferences and Location Based Community Trends." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/64283.
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Online social networks are experiencing an explosive growth in recent years in both the number of users and the amount of information shared. The users join these social networks to connect with each other, share, find content and disseminate information by sending short text messages in near realtime. As a result of the growth of social networks, the users are often experiencing information overload since they interact with many other users and read ever increasing content volume. Thus, finding the "matching" users and content is one of the key challenges for social networks sites. Recommendation systems have been proposed to help users cope with information overload by predicting the items that a user may be interested in.
The users' preferences are shaped by personal interests. At the same time, users are affected by their surroundings, as determined by their geographically located communities. Accordingly, our approach takes into account both personal interests and local communities. We first propose a new dynamic recommendation system model that provides better customized content to the user. That is, the model provides the user with the most important tweets according to his individual interests. We then analyze how changes in the surrounding environment can affect the user's experience. Specifically, we study how changes in the geographical community preferences can affect the individual user's interests. These community preferences are generally reflected in the localized trending topics. Consequently, we present TrendFusion, an innovative model that analyzes the trends propagation, predicts the localized diffusion of trends in social networks and recommends the most interesting trends to the user. Our performance evaluation demonstrate the effectiveness of the proposed recommendation system and shows that it improves the precision and recall of identifying important tweets by up to 36% and 80%, respectively. Results also show that TrendFusion
accurately predicts places in which a trend will appear, with 98% recall and 80% precision.Ph. D.
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JAIN, ABHA. "INTEREST MINING FOR RECOMMENDATION SYSTEM IN VIRTUAL COMMUNITIES." Thesis, 2015. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14297.
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As organizations, both business and research development continue to search better ways to exploit knowledge capital accumulated on the diversified Web; it fosters the need of collaboration among people with similar interest & expertise. With the advent and proliferation of the Internet and e-commerce, it is evident that the complexity of finding relevant information on the Web has become increasingly intricate and crucial. In fact, “information overload” on the Web is a well recognized problem, where users find it increasingly difficult to locate the right information at the right time. In response to the identified need for improved users' experience by personalizing what they see and using Web 2.0 as a novel platform for users’ participation, we propose the “COMREC system” that realizes a COMmunity interest based RECommendation system. In the proposed system firstly we build an interest similarity group, an online community which is a virtual space where people who are interested in a specific topic gather and discuss in depth a variety of sub-topics related to the topic using blogs. Expert identification involves finding experts on a given topic. Thus, once the group is constructed, as our next step we identify an expert from each of the group. Expert identification in online communities is of importance as online communities can be viewed as knowledge databases where knowledge is accumulated by interactions between the members. That is, we read articles in online communities to get information on specific topics and we tend to have more confidence in the articles written by experts. On the other hand, in terms of communication dynamics, online communities are spaces where non-experts can communicate with experts and communicating with experts is not only difficult but also expensive. Consequently, in the proposed COMREC system it’s the opinion of the identified expert within a virtual community built on shared interest that constitutes the recommendation. Eventually this paradigm helps to overcome the most prominent problem existent in collaborative filtering setting, the First-Rater or the cold- start problem, as in our proposed system it is only the expert whose recommendation is considered compared to systems which require a large set of customer preferences for predicting the new preferences accurately for effective Collaborative filtering-based recommendation. The initial results show that the interest mining for recommendation system in virtual communities for building COMREC system is a motivating technique.Dr. AKSHI KUMAR Assistant Professor DEPARTMENT OF SOFTWARE ENGINEERING DELHI TECHNOLOGICAL UNIVERSITY 2011
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Chen, I.-Ru, and 陳怡如. "A Study on the Recommendation System Based on Interest Map." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/32724019438553890272.
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碩士國立交通大學
經營管理研究所
95
By applying the concept of social network into recommendation system, we convert the relationships between interests into ‘Interest Map’, just as the social network looks like. According to the association strength, the system could recommend users interests from interests. The goal of study is to verify if the recommendation system based on Interest Map is feasible, and to compare the relative advantages of immediate computation, and dynamic system over the general recommendation systems. The relationship between two interests, here we call it association, is built when someone likes these two interests at the same time. Repeating the process of association-building, we make Interest Map. After recommendation, which is selected from the strongest strength of associations, we compute the precision rate and recall rate to verify if the recommendation system based on Interest Map is feadible. Our study suggests that the feature of immediate computation is achieved by the dynamic algorithm, and meets the need of routine update of the general recommendation systems. By this process, users could get the newest recommendation at any time, and may enhance the recommendation and user trust. Besides, dynamic system improves the efficiency of recommendation system. The feature of dynamic system allows the recommendation system to check the Interest Map inside and update in time, and makes the recommendation system at a prepared condition to response users’ request. Owing to the reasons above, the recommendation system based on Interest Map is feasible and has some relative advantages over the general recommendation systems.
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Wu, Chien-Liang, and 吳建良. "A Web Page Recommendation System Based on Clusters of Query Interest." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/90203635016331588291.
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碩士國立臺灣師範大學
資訊教育研究所
90
Most previous works on recommendation systems of web pages were designed based on collaborative filtering according to the clusters of user browsing behavior. In these approaches, a user only belongs to certain one cluster. If most users have multiple kinds of browsing interests, the number of users in the same cluster will be small and the information used for recommendation is limited. In addition, the information of users who have partially similar behavior is not considered. In this thesis, the strategies for constructing a query and recommendation system of web pages are proposed. First, the query keywords, browsed web pages, and user feedback values are extracted from web logs to be query transactions. A clustering algorithm is proposed to find the clusters of queries and related web pages, called the clusters of query interest , from the query transactions. A user who has multiple kinds of query interests can belong to more than one cluster. Then user query transactions are partitioned based on the clusters of query interest. In each partition, the association rules of queries and web pages are mined, where the support and confidence of rules are computed based on feedback values of users. According to the mined information, two main functions are provided in the system. A member user can ask a recommendation request. Based on clusters of query interest contained in the user profile, the highly associated web pages are recommended. On the other hand, an anonymous user can ask a query recommendation request to the system by giving query keywords. According to the cluster of query interest that the query keywords belong to, the highly associated web pages are returned as query results. Therefore, the query results will be more simplified and meet the requirements of most users.
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Yu, Wei Ting, and 魏廷宇. "The Study of Virtual Community Peer Recommendation System Based on Social Relationship." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/44751520102638124880.
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碩士輔仁大學
資訊管理學系
93
Knowledge has become the most important production element in the era of knowledge economy. Knowledge contains two parts - explicit knowledge and implicit one. If and only if we understand the two parts of knowledge, we say we understand knowledge. As the progress of information technology, virtual community in the Internet becomes the main platform to share knowledge. However, because of the characters of the post in the virtual community, the contented-based recommendation system does not fit. Moreover, collaborative recommendation system gets the problem called “ratings sparsity”. In the other way, the current recommendation systems do not consider the social relationship which is an important issue when people share knowledge. This thesis implemented 6 recommendation modules based on 6 measures which are used to estimate the social relationships between two members in a forum – a kind of virtual community in the Internet. When some member A creates a new topic, the recommendation modules will recommend people who are willing to discuss with A. This thesis used the data of a virtual community to understand the forecasting ability of the 6 recommendation modules based on social relationships. The experiment result shows that the greatest forecasting ability of recommendation module is based on the “mostpost” social relationship measure. In addition, computing relationship in the light of some specific members, not all of members, can increase the forecasting ability of recommendation modules, no matter based on what kind of measures.
Books on the topic "COMmunity interest based RECommendation system"
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Tietje, Christian, and Andrej Lang. Community Interests in World Trade Law. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198825210.003.0012.
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The authors argue that WTO law protects the community interest of promoting an essentially rules-based and fair world market. The core concern of WTO law is to protect trade-conducive structures that enable and further global economic activity for the purpose of generating overall welfare. The foundational principles of national treatment and most favored nation aim at protecting the equality of competitive conditions between WTO members. Derogations from WTO law are strictly limited whenever these principles are affected. The WTO enforcement regime entitles virtually every member to bring a complaint if a nondiscrimination obligation is breached. Although bilateral elements have long been dominant and still prominently exist in the WTO legal order, communal elements are slowly overlapping and superseding bilateral elements as part of the legal and institutional transformation of the world trade system brought about by the founding of the WTO, in particular the establishment of the Appellate Body.
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Galera, Giulia. Social and Solidarity Co-operatives. Edited by Jonathan Michie, Joseph R. Blasi, and Carlo Borzaga. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199684977.013.12.
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Over the past decades, new types of co-operatives with declared social goals have emerged in several countries in and outside Europe. Their development is above all connected to the engagement of co-operatives in the supply of general-interest services, which are carried out beyond the ‘boundaries’ of the co-ops’ membership, undermining the traditional model of co-operatives based on a single stakeholding system and on identifying members and users, and being ready to have additional bearers of interests sharing the duties and benefits of the organization. Drawing on selected country studies, this chapter focuses on new types of co-operatives with declared social goals, often turning into important providers of welfare and general-interest services and facilitators of work integration, which contribute to local development significantly. Selected countries where co-operatives have institutionalized their concern for community so as to pursue explicit general interest aims include Italy, Spain, France, Portugal, Greece, and South Korea.
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Glazov, M. M. Electron & Nuclear Spin Dynamics in Semiconductor Nanostructures. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198807308.001.0001.
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In recent years, the physics community has experienced a revival of interest in spin effects in solid state systems. On one hand, solid state systems, particularly semicon- ductors and semiconductor nanosystems, allow one to perform benchtop studies of quantum and relativistic phenomena. On the other hand, interest is supported by the prospects of realizing spin-based electronics where the electron or nuclear spins can play a role of quantum or classical information carriers. This book aims at rather detailed presentation of multifaceted physics of interacting electron and nuclear spins in semiconductors and, particularly, in semiconductor-based low-dimensional structures. The hyperfine interaction of the charge carrier and nuclear spins increases in nanosystems compared with bulk materials due to localization of electrons and holes and results in the spin exchange between these two systems. It gives rise to beautiful and complex physics occurring in the manybody and nonlinear system of electrons and nuclei in semiconductor nanosystems. As a result, an understanding of the intertwined spin systems of electrons and nuclei is crucial for in-depth studying and control of spin phenomena in semiconductors. The book addresses a number of the most prominent effects taking place in semiconductor nanosystems including hyperfine interaction, nuclear magnetic resonance, dynamical nuclear polarization, spin-Faraday and -Kerr effects, processes of electron spin decoherence and relaxation, effects of electron spin precession mode-locking and frequency focusing, as well as fluctuations of electron and nuclear spins.
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Wikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.
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The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the tools to perform inference and prediction in the presence of uncertainty. In particular, the field of spatial statistics considers inference and prediction for uncertain processes that exhibit dependence in space and/or time. Traditionally, this is done descriptively through the characterization of the first two moments of the process, one expressing the mean structure and one accounting for dependence through covariability.Historically, there are three primary areas of methodological development in spatial statistics: geostatistics, which considers processes that vary continuously over space; areal or lattice processes, which considers processes that are defined on a countable discrete domain (e.g., political units); and, spatial point patterns (or point processes), which consider the locations of events in space to be a random process. All of these methods have been used in the climate sciences, but the most prominent has been the geostatistical methodology. This methodology was simultaneously discovered in geology and in meteorology and provides a way to do optimal prediction (interpolation) in space and can facilitate parameter inference for spatial data. These methods rely strongly on Gaussian process theory, which is increasingly of interest in machine learning. These methods are common in the spatial statistics literature, but much development is still being done in the area to accommodate more complex processes and “big data” applications. Newer approaches are based on restricting models to neighbor-based representations or reformulating the random spatial process in terms of a basis expansion. There are many computational and flexibility advantages to these approaches, depending on the specific implementation. Complexity is also increasingly being accommodated through the use of the hierarchical modeling paradigm, which provides a probabilistically consistent way to decompose the data, process, and parameters corresponding to the spatial or spatio-temporal process.Perhaps the biggest challenge in modern applications of spatial and spatio-temporal statistics is to develop methods that are flexible yet can account for the complex dependencies between and across processes, account for uncertainty in all aspects of the problem, and still be computationally tractable. These are daunting challenges, yet it is a very active area of research, and new solutions are constantly being developed. New methods are also being rapidly developed in the machine learning community, and these methods are increasingly more applicable to dependent processes. The interaction and cross-fertilization between the machine learning and spatial statistics community is growing, which will likely lead to a new generation of spatial statistical methods that are applicable to climate science.
Book chapters on the topic "COMmunity interest based RECommendation system"
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He, Jianming, and Wesley W. Chu. "Design Considerations for a Social Network-Based Recommendation System (SNRS)." In Community-Built Databases, 73–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19047-6_4.
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Gurini, Davide Feltoni, Fabio Gasparetti, Alessandro Micarelli, and Giuseppe Sansonetti. "iSCUR: Interest and Sentiment-Based Community Detection for User Recommendation on Twitter." In User Modeling, Adaptation, and Personalization, 314–19. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08786-3_27.
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Interdonato, Roberto, and Andrea Tagarelli. "Personalized Recommendation of Points-of-Interest Based on Multilayer Local Community Detection." In Lecture Notes in Computer Science, 552–71. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67217-5_33.
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Wang, Yuehua, Zhinong Zhong, Anran Yang, and Ning Jing. "A Deep Point-of-Interest Recommendation System in Location-Based Social Networks." In Data Mining and Big Data, 547–54. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93803-5_51.
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Roy, Sohom, Sayan Kundu, Dhrubasish Sarkar, Chandan Giri, and Premananda Jana. "Community Detection and Design of Recommendation System Based on Criminal Incidents." In Advances in Intelligent Systems and Computing, 71–80. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7834-2_7.
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Santos, Filipe, Ana Almeida, Constantino Martins, Paulo Oliveira, and Ramiro Gonçalves. "Tourism Recommendation System based in User Functionality and Points-of-Interest Accessibility levels." In Advances in Intelligent Systems and Computing, 275–84. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48523-2_26.
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Ravi, Logesh, V. Subramaniyaswamy, V. Vijayakumar, Rutvij H. Jhaveri, and Jigarkumar Shah. "Hybrid User Clustering-Based Travel Planning System for Personalized Point of Interest Recommendation." In Advances in Intelligent Systems and Computing, 311–21. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9953-8_27.
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Ugli, Sadriddinov Ilkhomjon Rovshan, Doo-Soon Park, Daeyoung Kim, Yixuan Yang, Sony Peng, and Sophort Siet. "Movie Recommendation System Using Community Detection Based on the Girvan–Newman Algorithm." In Advances in Computer Science and Ubiquitous Computing, 599–605. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1252-0_80.
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Tang, Tiffany, and Gordon McCalla. "Beyond Learners’ Interest: Personalized Paper Recommendation Based on Their Pedagogical Features for an e-Learning System." In PRICAI 2004: Trends in Artificial Intelligence, 301–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28633-2_33.
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Massimo, David, and Francesco Ricci. "Next-POI Recommendations Matching User’s Visit Behaviour." In Information and Communication Technologies in Tourism 2021, 45–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65785-7_4.
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AbstractWe consider the urban tourism scenario, which is characterized by limited availability of information about individuals’ past behaviour. Our system goal is to identify relevant next Points of Interest (POIs) recommendations. We propose a technique that addresses the domain requirements by using clusters of users’ visits trajectories that show similar visit behaviour. Previous analysis clustered visit trajectories by aggregating trajectories that contain similar POIs. We compare our approach with a next-item recommendation state-of-the-art Neighbour-based model. The results show that customizing recommendations for clusters of users’ with similar behaviour yields superior performance on different quality dimensions of the recommendation.
Conference papers on the topic "COMmunity interest based RECommendation system"
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"MULTI-INTEREST COMMUNITIES AND COMMUNITY-BASED RECOMMENDATION." In 3rd International Conference on Web Information Systems and Technologies. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0001273800370045.
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Ahmed, Kazi Wasif, Md Mamunur Rashid, Md Kamrul Hasan, and Hasan Mahmud. "Cohesion based personalized community recommendation system." In 2015 18th International Conference on Computer and Information Technology (ICCIT). IEEE, 2015. http://dx.doi.org/10.1109/iccitechn.2015.7488038.
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Nandagawali, Priyanka A., and Jaikumar M. Patil. "Community based recommendation system based on products." In 2014 International Conference on Power Automation and Communication (INPAC). IEEE, 2014. http://dx.doi.org/10.1109/inpac.2014.6981153.
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Jain, Shainee, Tejaswi Pawar, Heth Shah, Omkar Morye, and Bhushan Patil. "Video Recommendation System Based on Human Interest." In 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT). IEEE, 2019. http://dx.doi.org/10.1109/iciict1.2019.8741428.
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Yu, Yunfei, and Yinghua Zhou. "Research on recommendation system based on interest clustering." In 11TH ASIAN CONFERENCE ON CHEMICAL SENSORS: (ACCS2015). Author(s), 2017. http://dx.doi.org/10.1063/1.4977377.
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Li, Chong, Kunyang Jia, Dan Shen, C. J. Richard Shi, and Hongxia Yang. "Hierarchical Representation Learning for Bipartite Graphs." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/398.
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Recommender systems on E-Commerce platforms track users' online behaviors and recommend relevant items according to each user’s interests and needs. Bipartite graphs that capture both user/item feature and use-item interactions have been demonstrated to be highly effective for this purpose. Recently, graph neural network (GNN) has been successfully applied in representation of bipartite graphs in industrial recommender systems. Providing individualized recommendation on a dynamic platform with billions of users is extremely challenging. A key observation is that the users of an online E-Commerce platform can be naturally clustered into a set of communities. We propose to cluster the users into a set of communities and make recommendations based on the information of the users in the community collectively. More specifically, embeddings are assigned to the communities and the user embedding is decomposed into two parts, each of which captures the community-level generalizations and individualized preferences respectively. The community embedding can be considered as an enhancement to the GNN methods that are inherently flat and do not learn hierarchical representations of graphs. The performance of the proposed algorithm is demonstrated on a public dataset and a world-leading E-Commerce company dataset.
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Byun, Sung-Woo, So-Min Lee, Seok-Pil Lee, Kwang-Yong Kim, and Cho Kee-Seong. "A recommendation system based on object of the interest." In 2016 18th International Conference on Advanced Communication Technology (ICACT). IEEE, 2016. http://dx.doi.org/10.1109/icact.2016.7423521.
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Byun, Sung-Woo, So-Min Lee, Seok-Pil Lee, Kwang-Yong Kim, and Kee-Seong Cho. "A recommendation system based on object of the interest." In 2016 18th International Conference on Advanced Communication Technology (ICACT). IEEE, 2016. http://dx.doi.org/10.1109/icact.2016.7423522.
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Yin, Bin, Yujiu Yang, and Wenhuang Liu. "ICSRec: Interest circle-based recommendation system incorporating social propagation." In 2014 4th IEEE International Conference on Information Science and Technology (ICIST). IEEE, 2014. http://dx.doi.org/10.1109/icist.2014.6920377.
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Zhou, Xuan, Xiaoming Wang, Guangyao Pang, Yaguang Lin, Pengfei Wan, and Meiling Ge. "Dual Attention-based Interest Network for Personalized Recommendation System." In 2021 IEEE 15th International Conference on Big Data Science and Engineering (BigDataSE). IEEE, 2021. http://dx.doi.org/10.1109/bigdatase53435.2021.00010.
Full textReports on the topic "COMmunity interest based RECommendation system"
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Yuebin, Xu. Development and Performance of the Elderly Care System in the People’s Republic of China. Asian Development Bank, August 2021. http://dx.doi.org/10.22617/wps210303-2.
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This paper attempts to understand and provide policy recommendations on the development and performance of the emerging elderly care system in the People’s Republic of China. The three-tiered elderly system in the country consists of home-based care as the core support, community-based care as necessary support, and residential care as supplementary support. The main policies and progress of the system are explained, including insights on how the government encourages private sector involvement. A key recommendation of this paper is the need for better integration of residential and home- and community-based care as part of the elderly care system.
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Ford, Adam T., Marcel Huijser, and Anthony P. Clevenger. Long-term responses of an ecological community to highway mitigation measures. Nevada Department of Transportation, June 2022. http://dx.doi.org/10.15788/ndot2022.06.
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In road mitigation systems characterized by multiple wildlife crossing structures (CS) and multiple-focal species, these species-specific design criteria are important to meeting management goals. CS types and locations are fixed in place and cannot be manipulated experimentally; long term studies may offer the best chance to inform evidence-based designs for new CS projects in the future. Long-term data from Banff National Park are uniquely posed to answer these critical questions. More recently, highway mitigation along US93 in Montana provides an additional case study with which to understand the responses of large animals to different CS designs. The purpose of this study is to identify factors affecting movement of large mammals through CS using data sets from both mitigation projects. Year-round monitoring of CS use was used in an analytical framework to address questions regarding species-specific and community level use of CS; design and habitat factors that best explain species-specific variation; and whether importance of design parameters changes over time. Over the 17 years of the Banff study, and the six years of the Montana study, CS facilitated over 200,000 crossing events at 55 locations. There were significant changes in annual crossing events over time. Variables associated with CS passage rates were species specific, but aligned with a few clusters of preference. With the exception of coyotes, all large carnivore species preferred open span bridges or overpasses to other CS types. In Montana, fencing was positively associated with passage rates for black bears and cougars. We found that wider CS tend to be preferred by most species, irrespective of their location. We also found that wider CS tend to have shorter ‘adaptation’ curves than narrower ones for grizzly bears, coyotes, cougars, and moose. Depending on the heterogeneity of the landscape near the highway, more CS may not create more crossing opportunities if local habitat conditions do not favor animals’ access to the road. At the scale of ecological communities, the flows of mass and energy are likely enough to alter the distribution of ecological processes in the Banff and Montana ecosystems. Our results highlight the value of long-term monitoring for assessing the effectiveness of mitigation measures. Our work confirms the species-specific nature of measure CS performance, leading to our primary recommendation that a diversity of CS designs be considered an essential part of a well-designed mitigation system for the large mammals of western North America. Short-term monitoring efforts may fail to accurately portray the ecological benefits of mitigation for populations and ecological communities. Our results will help to inform design and aid in the establishment of robust, long-term performance measures.
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Aharoni, Asaph, Zhangjun Fei, Efraim Lewinsohn, Arthur Schaffer, and Yaakov Tadmor. System Approach to Understanding the Metabolic Diversity in Melon. United States Department of Agriculture, July 2013. http://dx.doi.org/10.32747/2013.7593400.bard.
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Fruit quality is determined by numerous genetic factors that affect taste, aroma, color, texture, nutritional value and shelf life. To unravel the genetic components involved in the metabolic pathways behind these traits, the major goal of the project was to identify novel genes that are involved in, or that regulate, these pathways using correlation analysis between genotype, metabolite and gene expression data. The original and specific research objectives were: (1) Collection of replicated fruit from a population of 96 RI lines derived from parents distinguished by great diversity in fruit development and quality phenotypes, (2) Phenotypic and metabolic profiling of mature fruit from all 96 RI lines and their parents, (3) 454 pyrosequencing of cDNA representing mRNA of mature fruit from each line to facilitate gene expression analysis based on relative EST abundance, (4) Development of a database modeled after an existing database developed for tomato introgression lines (ILs) to facilitate online data analysis by members of this project and by researchers around the world. The main functions of the database will be to store and present metabolite and gene expression data so that correlations can be drawn between variation in target traits or metabolites across the RI population members and variation in gene expression to identify candidate genes which may impact phenotypic and chemical traits of interest, (5) Selection of RI lines for segregation and/or hybridization (crosses) analysis to ascertain whether or not genes associated with traits through gene expression/metabolite correlation analysis are indeed contributors to said traits. The overall research strategy was to utilize an available recombinant inbred population of melon (Cucumis melo L.) derived from phenotypically diverse parents and for which over 800 molecular markers have been mapped for the association of metabolic trait and gene expression QTLs. Transcriptomic data were obtained by high throughput sequencing using the Illumina platform instead of the originally planned 454 platform. The change was due to the fast advancement and proven advantages of the Illumina platform, as explained in the first annual scientific report. Metabolic data were collected using both targeted (sugars, organic acids, carotenoids) and non-targeted metabolomics analysis methodologies. Genes whose expression patterns were associated with variation of particular metabolites or fruit quality traits represent candidates for the molecular mechanisms that underlie them. Candidate genes that may encode enzymes catalyzingbiosynthetic steps in the production of volatile compounds of interest, downstream catabolic processes of aromatic amino acids and regulatory genes were selected and are in the process of functional analyses. Several of these are genes represent unanticipated effectors of compound accumulation that could not be identified using traditional approaches. According to the original plan, the Cucurbit Genomics Network (http://www.icugi.org/), developed through an earlier BARD project (IS-3333-02), was expanded to serve as a public portal for the extensive metabolomics and transcriptomic data resulting from the current project. Importantly, this database was also expanded to include genomic and metabolomic resources of all the cucurbit crops, including genomes of cucumber and watermelon, EST collections, genetic maps, metabolite data and additional information. In addition, the database provides tools enabling researchers to identify genes, the expression patterns of which correlate with traits of interest. The project has significantly expanded the existing EST resource for melon and provides new molecular tools for marker-assisted selection. This information will be opened to the public by the end of 2013, upon the first publication describing the transcriptomic and metabolomics resources developed through the project. In addition, well-characterized RI lines are available to enable targeted breeding for genes of interest. Segregation of the RI lines for specific metabolites of interest has been shown, demonstrating the utility in these lines and our new molecular and metabolic data as a basis for selection targeting specific flavor, quality, nutritional and/or defensive compounds. To summarize, all the specific goals of the project have been achieved and in many cases exceeded. Large scale trascriptomic and metabolomic resources have been developed for melon and will soon become available to the community. The usefulness of these has been validated. A number of novel genes involved in fruit ripening have been selected and are currently being functionally analyzed. We thus fully addressed our obligations to the project. In our view, however, the potential value of the project outcomes as ultimately manifested may be far greater than originally anticipated. The resources developed and expanded under this project, and the tools created for using them will enable us, and others, to continue to employ resulting data and discoveries in future studies with benefits both in basic and applied agricultural - scientific research.
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Rosen, Michael, C. Matthew Stewart, Hadi Kharrazi, Ritu Sharma, Montrell Vass, Allen Zhang, and Eric B. Bass. Potential Harms Resulting From Patient-Clinician Real-Time Clinical Encounters Using Video-based Telehealth: A Rapid Evidence Review. Agency for Healthcare Research and Quality (AHRQ), September 2023. http://dx.doi.org/10.23970/ahrqepc_mhs4telehealth.
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Objectives. To review the evidence on harms associated with patient-clinician real time encounters using video-based telehealth and determine the effectiveness of any related patient safety practices (PSPs). PSPs are interventions, strategies, or approaches intended to prevent or mitigate unintended consequences of healthcare delivery and improve patient safety. This review provides information that clinicians and health system leaders need to determine how to minimize harms from increasing real-time use of telehealth. Methods. We followed rapid review processes of the Agency for Healthcare Research and Quality Evidence-based Practice Center Program. We searched PubMed, EMBASE, and Cochrane to identify eligible studies published from 2012 to 2022, supplemented by a search for unpublished evaluations and white papers. Outcomes of interest included: adverse events (any harm to patients due to medical care), other specified harms (i.e., preventable hospitalizations, inappropriate treatment, missed or delayed diagnoses, duplication of services, privacy breaches), and implementation factors for any PSPs. Findings. Our search retrieved 7,155 citations, of which 23 studies (including 6 randomized controlled trials [RCTs]) were eligible for review. Fourteen studies reported on adverse events or unintended effects of telehealth; these studies were conducted in diverse settings, with four studies in behavioral health, two each in rehabilitation, transplant, and Parkinson’s care, and one each in postoperative, termination of pregnancy, community health, and hospital-at-home settings. Adverse events such as death, reoperation, infection, or major complications were infrequent in both telehealth and usual care groups, making it difficult to find statistically significant differences. One RCT found telehealth resulted in fewer medication errors than standard care. Thirteen studies examined preventable hospitalizations or emergency department (ED) visits and reported mixed findings; six of these studies were in postoperative care and two were in urological care. Of the 6 RCTs, 3 showed no difference in risk of hospitalization or ED visits for telehealth compared to usual care, and 3 showed reduced risk for patients receiving telehealth. We found no studies on the effectiveness of PSPs in reducing harms associated with real-time telehealth. Conclusions. Studies have evaluated the frequency and severity of harms associated with real-time video-based telehealth encounters between clinicians and patients, examining a variety of patient safety measures. Telehealth was not inferior to usual care in terms of hospitalizations or ED visits. No studies evaluated a specific PSP. More research is needed to improve understanding of harms associated with real-time use of telehealth and how to prevent or mitigate those harms.
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Doo, Johnny. Unsettled Issues Concerning eVTOL for Rapid-response, On-demand Firefighting. SAE International, August 2021. http://dx.doi.org/10.4271/epr2021017.
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Recent advancements of electric vertical take-off and landing (eVTOL) aircraft have generated significant interest within and beyond the traditional aviation industry, and many novel applications have been identified and are in development. One promising application for these innovative systems is in firefighting, with eVTOL aircraft complementing current firefighting capabilities to help save lives and reduce fire-induced damages. With increased global occurrences and scales of wildfires—not to mention the issues firefighters face during urban and rural firefighting operations daily—eVTOL technology could offer timely, on-demand, and potentially cost-effective aerial mobility capabilities to counter these challenges. Early detection and suppression of wildfires could prevent many fires from becoming large-scale disasters. eVTOL aircraft may not have the capacity of larger aerial assets for firefighting, but targeted suppression, potentially in swarm operations, could be valuable. Most importantly, on-demand aerial extraction of firefighters can be a crucial benefit during wildfire control operations. Aerial firefighter dispatch from local fire stations or vertiports can result in more effective operations, and targeted aerial fire suppression and civilian extraction from high-rise buildings could enhance capabilities significantly. There are some challenges that need to be addressed before the identified capabilities and benefits are realized at scale, including the development of firefighting-specific eVTOL vehicles; sense and avoid capabilities in complex, smoke-inhibited environments; autonomous and remote operating capabilities; charging system compatibility and availability; operator and controller training; dynamic airspace management; and vehicle/fleet logistics and support. Acceptance from both the first-responder community and the general public is also critical for the successful implementation of these new capabilities. The purpose of this report is to identify the benefits and challenges of implementation, as well as some of the potential solutions. Based on the rapid development progress of eVTOL aircraft and infrastructures with proactive community engagement, it is envisioned that these challenges can be addressed soon. NOTE: SAE EDGE™ Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. These reports are not intended to resolve the challenges they identify or close any topic to further scrutiny.
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Burns, Malcom, and Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, September 2023. http://dx.doi.org/10.46756/sci.fsa.ney927.
Full textAbstract:
The Genetic Technology (Precision Breeding) Act (England) aims to develop a science-based process for the regulation and authorisation of precision bred organisms (PBOs). PBOs are created by genetic technologies but exhibit changes which could have occurred through traditional processes. This current review, commissioned by the Food Standards Agency (FSA), aims to clarify existing terminologies, explore viable methods for the detection, identification, and quantification of products of precision breeding techniques, address and identify potential solutions to the analytical challenges presented, and provide recommendations for working towards an infrastructure to support detection of precision bred products in the future. The review includes a summary of the terminology in relation to analytical approaches for detection of precision bred products. A harmonised set of terminology contributes towards promoting further understanding of the common terms used in genome editing. A review of the current state of the art of potential methods for the detection, identification and quantification of precision bred products in the UK, has been provided. Parallels are drawn with the evolution of synergistic analytical approaches for the detection of Genetically Modified Organisms (GMOs), where molecular biology techniques are used to detect DNA sequence changes in an organism’s genome. The scope and limitations of targeted and untargeted methods are summarised. Current scientific opinion supports that modern molecular biology techniques (i.e., quantitative real-time Polymerase Chain Reaction (qPCR), digital PCR (dPCR) and Next Generation Sequencing (NGS)) have the technical capability to detect small alterations in an organism’s genome, given specific prerequisites of a priori information on the DNA sequence of interest and of the associated flanking regions. These techniques also provide the best infra-structure for developing potential approaches for detection of PBOs. Should sufficient information be known regarding a sequence alteration and confidence can be attributed to this being specific to a PBO line, then detection, identification and quantification can potentially be achieved. Genome editing and new mutagenesis techniques are umbrella terms, incorporating a plethora of approaches with diverse modes of action and resultant mutational changes. Generalisations regarding techniques and methods for detection for all PBO products are not appropriate, and each genome edited product may have to be assessed on a case-by-case basis. The application of modern molecular biology techniques, in isolation and by targeting just a single alteration, are unlikely to provide unequivocal evidence to the source of that variation, be that as a result of precision breeding or as a result of traditional processes. In specific instances, detection and identification may be technically possible, if enough additional information is available in order to prove that a DNA sequence or sequences are unique to a specific genome edited line (e.g., following certain types of Site-Directed Nucelase-3 (SDN-3) based approaches). The scope, gaps, and limitations associated with traceability of PBO products were examined, to identify current and future challenges. Alongside these, recommendations were made to provide the infrastructure for working towards a toolkit for the design, development and implementation of analytical methods for detection of PBO products. Recognition is given that fully effective methods for PBO detection have yet to be realised, so these recommendations have been made as a tool for progressing the current state-of-the-art for research into such methods. Recommendations for the following five main challenges were identified. Firstly, PBOs submitted for authorisation should be assessed on a case-by-case basis in terms of the extent, type and number of genetic changes, to make an informed decision on the likelihood of a molecular biology method being developed for unequivocal identification of that specific PBO. The second recommendation is that a specialist review be conducted, potentially informed by UK and EU governmental departments, to monitor those PBOs destined for the authorisation process, and actively assess the extent of the genetic variability and mutations, to make an informed decision on the type and complexity of detection methods that need to be developed. This could be further informed as part of the authorisation process and augmented via a publicly available register or database. Thirdly, further specialist research and development, allied with laboratory-based evidence, is required to evaluate the potential of using a weight of evidence approach for the design and development of detection methods for PBOs. This concept centres on using other indicators, aside from the single mutation of interest, to increase the likelihood of providing a unique signature or footprint. This includes consideration of the genetic background, flanking regions, off-target mutations, potential CRISPR/Cas activity, feasibility of heritable epigenetic and epitranscriptomic changes, as well as supplementary material from supplier, origin, pedigree and other documentation. Fourthly, additional work is recommended, evaluating the extent/type/nature of the genetic changes, and assessing the feasibility of applying threshold limits associated with these genetic changes to make any distinction on how they may have occurred. Such a probabilistic approach, supported with bioinformatics, to determine the likelihood of particular changes occurring through genome editing or traditional processes, could facilitate rapid classification and pragmatic labelling of products and organisms containing specific mutations more readily. Finally, several scientific publications on detection of genome edited products have been based on theoretical principles. It is recommended to further qualify these using evidenced based practical experimental work in the laboratory environment. Additional challenges and recommendations regarding the design, development and implementation of potential detection methods were also identified. Modern molecular biology-based techniques, inclusive of qPCR, dPCR, and NGS, in combination with appropriate bioinformatics pipelines, continue to offer the best analytical potential for developing methods for detecting PBOs. dPCR and NGS may offer the best technical potential, but qPCR remains the most practicable option as it is embedded in most analytical laboratories. Traditional screening approaches, similar to those for conventional transgenic GMOs, cannot easily be used for PBOs due to the deficit in common control elements incorporated into the host genome. However, some limited screening may be appropriate for PBOs as part of a triage system, should a priori information be known regarding the sequences of interest. The current deficit of suitable methods to detect and identify PBOs precludes accurate PBO quantification. Development of suitable reference materials to aid in the traceability of PBOs remains an issue, particularly for those PBOs which house on- and off-target mutations which can segregate. Off-target mutations may provide an additional tool to augment methods for detection, but unless these exhibit complete genetic linkage to the sequence of interest, these can also segregate out in resulting generations. Further research should be conducted regarding the likelihood of multiple mutations segregating out in a PBO, to help inform the development of appropriate PBO reference materials, as well as the potential of using off-target mutations as an additional tool for PBO traceability. Whilst recognising the technical challenges of developing and maintaining pan-genomic databases, this report recommends that the UK continues to consider development of such a resource, either as a UK centric version, or ideally through engagement in parallel EU and international activities to better achieve harmonisation and shared responsibilities. Such databases would be an invaluable resource in the design of reliable detection methods, as well as for confirming that a mutation is as a result of genome editing. PBOs and their products show great potential within the agri-food sector, necessitating a science-based analytical framework to support UK legislation, business and consumers. Differentiating between PBOs generated through genome editing compared to organisms which exhibit the same mutational change through traditional processes remains analytically challenging, but a broad set of diagnostic technologies (e.g., qPCR, NGS, dPCR) coupled with pan-genomic databases and bioinformatics approaches may help contribute to filling this analytical gap, and support the safety, transparency, proportionality, traceability and consumer confidence associated with the UK food chain.