Relevant bibliographies by topics / User Interest Profiling

Academic literature on the topic 'User Interest Profiling'

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Published: 10 March 2023

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Journal articles on the topic "User Interest Profiling"

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Liang, Shangsong. "Collaborative, Dynamic and Diversified User Profiling." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4269–76. http://dx.doi.org/10.1609/aaai.v33i01.33014269.

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In this paper, we study the problem of dynamic user profiling in the context of streams of short texts. Previous work on user profiling works with long documents, do not consider collaborative information, and do not diversify the keywords for profiling users’ interests. In contrast, we address the problem by proposing a user profiling algorithm (UPA), which consists of two models: the proposed collaborative interest tracking topic model (CITM) and the proposed streaming keyword diversification model (SKDM). UPA first utilizes CITM to collaboratively track each user’s and his followees’ dynamic interest distributions in the context of streams of short texts, and then utilizes SKDM to obtain top-k relevant and diversified keywords to profile users’ interests at a specific point in time. Experiments were conducted on a Twitter dataset and we found that UPA outperforms state-of-the-art non-dynamic and dynamic user profiling algorithms.
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Lu, Junru, Le Chen, Kongming Meng, Fengyi Wang, Jun Xiang, Nuo Chen, Xu Han, and Binyang Li. "Identifying User Profile by Incorporating Self-Attention Mechanism based on CSDN Data Set." Data Intelligence 1, no. 2 (May 2019): 160–75. http://dx.doi.org/10.1162/dint_a_00009.

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With the popularity of social media, there has been an increasing interest in user profiling and its applications nowadays. This paper presents our system named UIR-SIST for User Profiling Technology Evaluation Campaign in SMP CUP 2017. UIR-SIST aims to complete three tasks, including keywords extraction from blogs, user interests labeling and user growth value prediction. To this end, we first extract keywords from a user's blog, including the blog itself, blogs on the same topic and other blogs published by the same user. Then a unified neural network model is constructed based on a convolutional neural network (CNN) for user interests tagging. Finally, we adopt a stacking model for predicting user growth value. We eventually receive the sixth place with evaluation scores of 0.563, 0.378 and 0.751 on the three tasks, respectively.
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Tang, Xiaoyu, and Qingtian Zeng. "Keyword clustering for user interest profiling refinement within paper recommender systems." Journal of Systems and Software 85, no. 1 (January 2012): 87–101. http://dx.doi.org/10.1016/j.jss.2011.07.029.

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You, Quanzeng, Sumit Bhatia, and Jiebo Luo. "A picture tells a thousand words—About you! User interest profiling from user generated visual content." Signal Processing 124 (July 2016): 45–53. http://dx.doi.org/10.1016/j.sigpro.2015.10.032.

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Yang, Chunfeng, Yipeng Zhou, and Dah Ming Chiu. "Who Are Like-Minded: Mining User Interest Similarity in Online Social Networks." Proceedings of the International AAAI Conference on Web and Social Media 10, no. 1 (August 4, 2021): 731–34. http://dx.doi.org/10.1609/icwsm.v10i1.14779.

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In this paper, we mine and learn to predict how similar a pair of users’ interests towards videos are, based on demographic, social and interest information of these users. We use the video access patterns of active users as ground truth. We adopt tag-based user profiling to establish this ground truth. We then show the effectiveness of the different features, and their combinations and derivatives, in predicting user interest similarity, based on different machine-learning methods for combining multiple features. We propose a hybrid tree-encoded linear model for combining the features, and show that it out-performs other linear and tree-based models. Our methods can be used to predict user interest similarity when the ground-truth is not available, e.g. for new users, or inactive users whose interests may have changed from old access data, and is useful for video recommendation.
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Shen, Jiaxing, Jiannong Cao, Oren Lederman, Shaojie Tang, and Alex “Sandy” Pentland. "User Profiling Based on Nonlinguistic Audio Data." ACM Transactions on Information Systems 40, no. 1 (January 31, 2022): 1–23. http://dx.doi.org/10.1145/3474826.

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User profiling refers to inferring people’s attributes of interest ( AoIs ) like gender and occupation, which enables various applications ranging from personalized services to collective analyses. Massive nonlinguistic audio data brings a novel opportunity for user profiling due to the prevalence of studying spontaneous face-to-face communication. Nonlinguistic audio is coarse-grained audio data without linguistic content. It is collected due to privacy concerns in private situations like doctor-patient dialogues. The opportunity facilitates optimized organizational management and personalized healthcare, especially for chronic diseases. In this article, we are the first to build a user profiling system to infer gender and personality based on nonlinguistic audio. Instead of linguistic or acoustic features that are unable to extract, we focus on conversational features that could reflect AoIs. We firstly develop an adaptive voice activity detection algorithm that could address individual differences in voice and false-positive voice activities caused by people nearby. Secondly, we propose a gender-assisted multi-task learning method to combat dynamics in human behavior by integrating gender differences and the correlation of personality traits. According to the experimental evaluation of 100 people in 273 meetings, we achieved 0.759 and 0.652 in F1-score for gender identification and personality recognition, respectively.
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Movahedian, Hamed, and Mohammad Reza Khayyambashi. "Folksonomy-based user interest and disinterest profiling for improved recommendations: An ontological approach." Journal of Information Science 40, no. 5 (June 19, 2014): 594–610. http://dx.doi.org/10.1177/0165551514539870.

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Godoy, D., and A. Amandi. "Interest Drifts in User Profiling: A Relevance-Based Approach and Analysis of Scenarios." Computer Journal 52, no. 7 (January 4, 2008): 771–88. http://dx.doi.org/10.1093/comjnl/bxm107.

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Worzella, Tracy, Matt Butzler, Jacquelyn Hennek, Seth Hanson, Laura Simdon, Said Goueli, Cris Cowan, and Hicham Zegzouti. "A Flexible Workflow for Automated Bioluminescent Kinase Selectivity Profiling." SLAS TECHNOLOGY: Translating Life Sciences Innovation 22, no. 2 (November 15, 2016): 153–62. http://dx.doi.org/10.1177/2211068216677248.

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Kinase profiling during drug discovery is a necessary process to confirm inhibitor selectivity and assess off-target activities. However, cost and logistical limitations prevent profiling activities from being performed in-house. We describe the development of an automated and flexible kinase profiling workflow that combines ready-to-use kinase enzymes and substrates in convenient eight-tube strips, a bench-top liquid handling device, ADP-Glo Kinase Assay (Promega, Madison, WI) technology to quantify enzyme activity, and a multimode detection instrument. Automated methods were developed for kinase reactions and quantification reactions to be assembled on a Gilson (Middleton, WI) PIPETMAX, following standardized plate layouts for single- and multidose compound profiling. Pipetting protocols were customized at runtime based on user-provided information, including compound number, increment for compound titrations, and number of kinase families to use. After the automated liquid handling procedures, a GloMax Discover (Promega) microplate reader preloaded with SMART protocols was used for luminescence detection and automatic data analysis. The functionality of the automated workflow was evaluated with several compound-kinase combinations in single-dose or dose-response profiling formats. Known target-specific inhibitions were confirmed. Novel small molecule-kinase interactions, including off-target inhibitions, were identified and confirmed in secondary studies. By adopting this streamlined profiling process, researchers can quickly and efficiently profile compounds of interest on site.
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Kamel Ghalibaf, Azadeh, Zahra Mazloum Khorasani, Mahdi Gholian Aval, and Mahmood Tara. "Aspects of User Profiling in Computer-based Health Information Tailoring Systems: A Narrative Review." Medical Technologies Journal 1, no. 4 (November 29, 2017): 105–6. http://dx.doi.org/10.26415/2572-004x-vol1iss4p105-106.

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Introduction: The recent shift from the conventional physician-centered approach to the more polpular approach that with the focuse on patient as the center of healthcare, emphaizes on the critical role of informing and educating patients. Studies shown that tailoring health information to the needs of individuals is more effective than generic materials. Recent improvements in the fields of computer science and Information Communication Technology have made it possible to computerize such an adaptation process. Information tailoring systems use an internal representation of user conditions and needs, which is referred to as a “user model” or “user profile.” A user profile represents the system’s beliefs about the user. Hence, it may simply contain demographic information or sophisticated factors such as the state of the disease, user’s attitude, interest, preference, and knowledge. The user profile is known as the basis for designing other system components and has a great impact on the acceptance of the system by the user and the quality of the tailored information. To the best of our knowledge, no studies have been conducted so far to analyze and classify user profile aspects and characteristics. In this systematic narrative review, we aim to provide aspects of profiling in health information tailoring systems based on literature from different disciplines. Methods: comprehensive searches of the PubMed and Scopus databases have been conducted. We searched among English papers with publishing dates ranging from 1990 onward; since that is when computer-tailoring first appeared within the literature. we have devised a list of terms pertinent to the main concepts of computer-tailoring and used a qualitative–interpretive approach for data extraction. Results: Analyzing the data from 32 eligible studies, we found three aspects in designing a tailoring user profile. Each aspect with its characteristics are provided below: 1-Identifying common factors used in profiles and classifying these factors thematically, which has three attributes: The number of factors used to design the user profile and their diversity (e.g. demographic,clinical,behavioral information, learning style and so forth) The approaches used to Identify effective factors in tailoring (e.g. evidence-based, avalible data sources) Attributes of the factors (e.g. long-term/short-term, static/dynamic) 2-Data collection tools and methods, which has two attributes: Data collection methods (e.g. explicit, implicit, mixed) Assessment tool (e.g. questionnaire, patient record) 3-Data interpretation that demonstrates to what extent the collected data needs to be analyzed to use in tailoring. we have also identified two main approaches regarding tailoring: public health and computational tailoring. Public Health communication researcher has relied greatly on health behavior models but generally has used simpler technological approaches, whereas computer science employed more advanced technological approaches but integrated behavior theory to a lesser extent. These two approaches complete each other to provide the necessary requirements for designing a practical tailoring system in future studies. Conclusion: In this study we investigate different aspects of designing a user profile in health information tailoring systems. The proposed model is a valuable guide for new researchers in the field. Results from this review provide a comprehensive overview of the field and will help researchers to combine effective methods from across the disciplines in future research.
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Dissertations / Theses on the topic "User Interest Profiling"

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Dokoohaki, Nima. "Trust-Based User Profiling." Doctoral thesis, KTH, Programvaruteknik och Datorsystem, SCS, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-118488.

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We have introduced the notion of user profiling with trust, as a solution to theproblem of uncertainty and unmanageable exposure of personal data duringaccess, retrieval and consumption by web applications. Our solution sug-gests explicit modeling of trust and embedding trust metrics and mechanismswithin very fabric of user profiles. This has in turn allowed information sys-tems to consume and understand this extra knowledge in order to improveinteraction and collaboration among individuals and system. When formaliz-ing such profiles, another challenge is to realize increasingly important notionof privacy preferences of users. Thus, the profiles are designed in a way toincorporate preferences of users allowing target systems to understand pri-vacy concerns of users during their interaction. A majority of contributionsof this work had impact on profiling and recommendation in digital librariescontext, and was implemented in the framework of EU FP7 Smartmuseumproject. Highlighted results start from modeling of adaptive user profilesincorporating users taste, trust and privacy preferences. This in turn led toproposal of several ontologies for user and content characteristics modeling forimproving indexing and retrieval of user content and profiles across the plat-form. Sparsity and uncertainty of profiles were studied through frameworksof data mining and machine learning of profile data taken from on-line so-cial networks. Results of mining and population of data from social networksalong with profile data increased the accuracy of intelligent suggestions madeby system to improving navigation of users in on-line and off-line museum in-terfaces. We also introduced several trust-based recommendation techniquesand frameworks capable of mining implicit and explicit trust across ratingsnetworks taken from social and opinion web. Resulting recommendation al-gorithms have shown to increase accuracy of profiles, through incorporationof knowledge of items and users and diffusing them along the trust networks.At the same time focusing on automated distributed management of profiles,we showed that coverage of system can be increased effectively, surpassingcomparable state of art techniques. We have clearly shown that trust clearlyelevates accuracy of suggestions predicted by system. To assure overall pri-vacy of such value-laden systems, privacy was given a direct focus when archi-tectures and metrics were proposed and shown that a joint optimal setting foraccuracy and perturbation techniques can maintain accurate output. Finally,focusing on hybrid models of web data and recommendations motivated usto study impact of trust in the context of topic-driven recommendation insocial and opinion media, which in turn helped us to show that leveragingcontent-driven and tie-strength networks can improve systems accuracy forseveral important web computing tasks.

QC 20130219

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Arikan, Erinc. "Attack profiling for DDoS benchmarks." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file Mb., 96 p, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:1435821.

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Farahbakhsh, Reza. "Profiling professional and regular users on popular Internet services based on implementation of large scale Internet measurement tools." Thesis, Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0012/document.

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Les services Internet populaires modèlent et remodèlent fondamentalement les moyens traditionnels de communication des personnes, ayant ainsi un impact majeur sur leur vie sociale. Deux des services Internet très populaires avec cette caractéristique sont les Réseaux sociaux en ligne (OSN) et les systèmes Peer-to-Peer (P2P). Les ONS fournissent un environnement virtuel où les gens peuvent partager leurs informations et leurs intérêts tout en étant en contact avec d'autres personnes. D'autre part, les systèmes P2P, qui sont toujours l'un des services populaires avec une grande proportion de l'ensemble du trafic Internet, offrent une occasion en or pour leurs clients de partager un type de contenu différent, y compris le contenu protégé. En dehors de l'énorme popularité des ONS et des systèmes de P2P parmi les utilisateurs réguliers, ils sont intensivement utilisés par les professionnels (grandes entreprises, politiciens, athlètes, célébrités en cas d'ONS et éditeurs de contenu professionnels en cas de P2P) afin d'interagir avec les gens à des fins différentes (campagnes marketing, les commentaires des clients, amélioration de la réputation publique, etc.) Dans cette thèse, nous caractérisons le comportement des utilisateurs réguliers et professionnels dans les deux services mentionnés populaires (ONS et P2P) en termes de stratégies de publication, de consommation de contenu et d'analyse comportementale. À cette fin, cinq de nos études menées sont présentées dans ce manuscrit comme suit: - "L'évolution des contenus multimédias", qui présente une analyse approfondie sur l'évolution du contenu multimédia disponible en BitTorrent en se concentrant sur quatre mesures pertinentes à travers différentes catégories de contenu : la disponibilité du contenu, la popularité du contenu, la taille de contenu et les commentaires de l'utilisateur - "La réaction des utilisateurs professionnels face aux actions de lutte contre le piratage", en examinant l'impact de deux grandes actions de lutte contre le piratage - la fermeture de Megaupload et la mise en œuvre de la loi anti-piratage française (HADOPI) - sur le comportement des publicateurs professionnels dans le plus grand portail de BitTorrent qui sont les principaux fournisseurs de contenu en ligne protégé. - "La quantité d'informations divulguées sur Facebook", en enquêtant sur l'exposition publique des profils utilisateurs, une grande base de données comprenant un demi-million d'utilisateurs réguliers. - "Les utilisateurs professionnels Cross Posting Activity», en analysant le modèle de publication des utilisateurs professionnels de mêmes informations sur trois grands ONS à savoir Facebook, Google+ et Twitter. - "Les stratégies des utilisateurs professionnels dans les ONS", où nous étudions la stratégie globale d'utilisateurs professionnels par secteur (par exemple, les entreprises de voitures, l'habillement, politiques, etc.) sur Facebook, Google+ et Twitter. Les résultats de cette thèse fournissent une vision d'ensemble pour comprendre certains aspects comportementaux importants de différents types d'utilisateurs des services Internet populaires et ces contributions peuvent être utilisées dans divers domaines (par exemple analyse de campagne marketing et publicité, etc.) et les différentes parties peuvent bénéficier des résultats et des méthodologies mises en œuvre telles que les FAI et les propriétaires des services pour leur planification ou l'expansion des services actuels à venir, ainsi que les professionnels pour accroître leur succès sur les médias sociaux
Popular Internet services are fundamentally shaping and reshaping traditional ways of people communication, thus having a major impact on their social life. Two of the very popular Internet services with this characteristic are Online Social Networks (OSNs) and Peer-to-Peer (P2P) systems. OSNs provide a virtual environment where people can share their information and interests as well as being in contact with other people. On the other hand, P2P systems, which are still one of the popular services with a large proportion of the whole Internet traffic, provide a golden opportunity for their customers to share different type of content including copyrighted content. Apart from the huge popularity of OSNs and P2P systems among regular users, they are being intensively used by professional players (big companies, politician, athletes, celebrities in case of OSNs and professional content publishers in case of P2P) in order to interact with people for different purposes (marketing campaigns, customer feedback, public reputation improvement, etc.). In this thesis, we characterize the behavior of regular and professional users in the two mentioned popular services (OSNs and P2P systems) in terms of publishing strategies, content consumption and behavioral analysis. To this end, five of our conducted studies are presented in this manuscript as follows: - “The evolution of multimedia contents", which presents a thorough analysis on the evolution of multimedia content available in BitTorrent by focusing on four relevant metrics across different content categories: content availability, content popularity, content size and user's feedback. - “The reaction of professional users to antipiracy actions", by examining the impact of two major antipiracy actions, the closure of Megaupload and the implementation of the French antipiracy law (HADOPI), on professional publishers behavior in the largest BitTorrent portal who are major providers of online copyrighted content. - “The amount of disclosed information on Facebook", by investigating the public exposure of Facebook users' profile attributes in a large dataset including half million regular users. - “Professional users Cross Posting Activity", by analyzing the publishing pattern of professional users which includes same information over three major OSNs namely Facebook, Google+ and Twitter. - “Professional Users' Strategies in OSNs", where we investigate the global strategy of professional users by sector (e.g., Cars companies, Clothing companies, Politician, etc.) over Facebook, Google+ and Twitter. The outcomes of this thesis provide an overall vision to understand some important behavioral aspects of different types of users on popular Internet services and these contributions can be used in various domains (e.g. marketing analysis and advertising campaign, etc.) and different parties can benefit from the results and the implemented methodologies such as ISPs and owners of the Services for their future planning or expansion of the current services as well as professional players to increase their success on social media
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Tseng, Hsin-Cheng, and 曾信誠. "Ontology-based User Profiling with Interest Extraction and Privacy Control." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/88644564409153510656.

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碩士
國立東華大學
資訊工程學系
92
With the increasing of information on the web, it is a immediately request to extract what user really need among the almost unlimited information resource. Many researches have developed approaches to provide personalized services. However, most personalized services involved with user profiles which means privacy issue has to be concerned. Protecting user profile is getting more and more important. The trade-off between personalized services and user privacy is what we try to solve. Our research provides personalized services and protected privacy at the same time. We use concept query frequency as our indicator and the result of ontology inference as hidden interest indicator. In our research, long-term interests are regarded as unobvious factor and short-term interests are recently focuses. By separating user interests into two sets, we can describe user behaviors more precisely. We also keep XML-formed user profile in client, and develop different level of releasing policy. User may control what he wants to release by selecting the policies. So the sever can’t get what user doesn’t want to reveal, and the privacy is protected
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Bartoli, Federico. "User interest profiling by real time person detection and coarse gaze estimation." Doctoral thesis, 2017. http://hdl.handle.net/2158/1091488.

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This thesis makes different contributions to person detection, coarse gaze estimation and user interest profiling. We have proposed two methods to reduce the complexity of a multi-scale person detection, which address the two fundamental bottlenecks of cascade detectors: the number of weak classifiers that need to be evaluated in each cascade, and the total number of detection windows that must be evaluated. As regards the task of people profiling, we proposed a strategy to profile the attention of people moving in a known space, exploiting coarse gaze estimation and a novel model based on optical flow to improve attention prediction, without the need of a tracker.
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"Connecting Users with Similar Interests for Group Understanding." Doctoral diss., 2013. http://hdl.handle.net/2286/R.I.17780.

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abstract: In most social networking websites, users are allowed to perform interactive activities. One of the fundamental features that these sites provide is to connecting with users of their kind. On one hand, this activity makes online connections visible and tangible; on the other hand, it enables the exploration of our connections and the expansion of our social networks easier. The aggregation of people who share common interests forms social groups, which are fundamental parts of our social lives. Social behavioral analysis at a group level is an active research area and attracts many interests from the industry. Challenges of my work mainly arise from the scale and complexity of user generated behavioral data. The multiple types of interactions, highly dynamic nature of social networking and the volatile user behavior suggest that these data are complex and big in general. Effective and efficient approaches are required to analyze and interpret such data. My work provide effective channels to help connect the like-minded and, furthermore, understand user behavior at a group level. The contributions of this dissertation are in threefold: (1) proposing novel representation of collective tagging knowledge via tag networks; (2) proposing the new information spreader identification problem in egocentric soical networks; (3) defining group profiling as a systematic approach to understanding social groups. In sum, the research proposes novel concepts and approaches for connecting the like-minded, enables the understanding of user groups, and exposes interesting research opportunities.
Dissertation/Thesis
Ph.D. Computer Science 2013
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Books on the topic "User Interest Profiling"

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International WEBKDD '99 Workshop (1999 San Diego, Calif.). Web usage analysis and user profiling: International WEBKDD '99 Workshop, San Diego, CA, USA, August 15, 1999 : revised papers. Berlin ; New York: Springer, 2000.

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Institute, Practising Law, ed. Tracking and targeting customers and prospects online, on mobile devices, and in social media 2012. New York, N.Y: Practising Law Institute, 2012.

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Institute, Practising Law, ed. Tracking and targeting customers and prospects online, on mobile devices, and in social media 2013. New York, N.Y: Practising Law Institute, 2013.

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Masand, Brij, and Myra Spiliopoulou. Web Usage Analysis and User Profiling: International WEBKDD'99 Workshop San Diego, CA, USA, August 15, 1999 Revised Papers. Springer, 2003.

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Masand, Brij. Web Usage Analysis and User Profiling: International WEBKDD'99 Workshop San Diego, CA, USA, August 15, 1999 Revised Papers (Lecture Notes in Computer Science). Springer, 2000.

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Cheng, Russell. Change-Point Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0011.

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This chapter investigates change-point (hazard rate) probability models for the random survival time in some population of interest. A parametric probability distribution is assumed with parameters to be estimated from a sample of observed survival times. If a change-point parameter, denoted by τ‎, is included to represent the time at which there is a discrete change in hazard rate, then the model is non-standard. The profile log-likelihood, with τ‎ as profiling parameter, has a discontinuous jump at every τ‎ equal to a sampled value, becoming unbounded as τ‎ tends to the largest observation. It is known that maximum likelihood estimation can still be used provided the range of τ‎ is restricted. It is shown that the alternative maximum product of spacings method is consistent without restriction on τ‎. Censored observations which commonly occur in survival-time data can be accounted for using Kaplan-Meier estimation. A real data numerical example is given.
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Book chapters on the topic "User Interest Profiling"

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Wandabwa, Herman, M. Asif Naeem, Farhaan Mirza, Russel Pears, and Andy Nguyen. "Multi-interest User Profiling in Short Text Microblogs." In Designing for Digital Transformation. Co-Creating Services with Citizens and Industry, 154–68. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64823-7_15.

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Orlandi, Fabrizio. "Multi-source Provenance-aware User Interest Profiling on the Social Semantic Web." In User Modeling, Adaptation, and Personalization, 378–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31454-4_40.

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Broder, Alan J. "Data Mining the Internet and Privacy." In Web Usage Analysis and User Profiling, 56–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44934-5_4.

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Murray, Dan, and Kevan Durrell. "Inferring Demographic Attributes of Anonymous Internet Users." In Web Usage Analysis and User Profiling, 7–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44934-5_1.

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Baumgarten, Matthias, Alex G. Büchner, Sarabjot S. Anand, Maurice D. Mulvenna, and John G. Hughes. "User-Driven Navigation Pattern Discovery from Internet Data." In Web Usage Analysis and User Profiling, 74–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44934-5_5.

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Silem, Abd El Heq, Hajer Taktak, and Faouzi Moussa. "Dynamic User Interests Profiling Using Fuzzy Logic Application." In Advanced Information Networking and Applications, 968–79. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44041-1_84.

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Anand, Deepa, and Bonson Sebastian Mampilli. "User Profiling Based on Keyword Clusters for Improved Recommendations." In Distributed Computing and Internet Technology, 176–87. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04483-5_19.

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Rongen, P. H. H., J. Schröder, F. P. M. Dignum, and J. Moorman. "A Multi Agent Approach to Interest Profiling of Users." In Multi-Agent Systems and Applications IV, 326–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11559221_33.

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Kook, Hyung Joon. "Profiling Multiple Domains of User Interests and Using Them for Personalized Web Support." In Lecture Notes in Computer Science, 512–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11538356_53.

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Meier, Michael, and Megan J. Wilson. "Using RNA-Seq for Transcriptome Profiling of Botrylloides sp. Regeneration." In Methods in Molecular Biology, 599–615. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2172-1_32.

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AbstractThe decrease in sequencing costs and technology improvements has led to the adoption of RNA-sequencing to profile transcriptomes from further non-traditional regeneration model organisms such as the colonial ascidian Botrylloides leachii. The relatively unbiased way in which transcripts are identified and quantified makes this technique suitable to detect large-scale changes in expression, and the identification of novel transcripts and isoforms. Of particular interest to many researchers is the discovery of differentially expressed transcripts across different treatment conditions or stages of regeneration. This protocol describes a workflow starting from processing raw sequencing reads, mapping reads, assembly of transcripts, and measuring their abundance, creating lists of differentially expressed genes and their biological interpretation using gene ontologies. All programs used in this protocol are open-source software tools and freely available.
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Conference papers on the topic "User Interest Profiling"

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Zeb, Muhammad Ali, and Maria Fasli. "Interest Aware Recommendations Based on Adaptive User Profiling." In 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 2011. http://dx.doi.org/10.1109/wi-iat.2011.234.

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Bartoli, Federico, Giuseppe Lisanti, Lorenzo Seidenari, and Alberto Del Bimbo. "User interest profiling using tracking-free coarse gaze estimation." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7899904.

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Zhou, Pengyuan, and Jussi Kangasharju. "Profiling and Grouping Users to Edge Resources According to User Interest Similarity." In the 2016 ACM Workshop. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/3010079.3010081.

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Tang, Xiaoyu, Yue Xu, and Shlomo Geva. "Integrating Time Forgetting Mechanisms into Topic-Based User Interest Profiling." In 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 2013. http://dx.doi.org/10.1109/wi-iat.2013.132.

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Ali, Iqra, and M. Asif Naeem. "Identifying and Profiling User Interest over time using Social Data." In 2022 24th International Multitopic Conference (INMIC). IEEE, 2022. http://dx.doi.org/10.1109/inmic56986.2022.9972955.

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Chen, Weijian, Yulong Gu, Zhaochun Ren, Xiangnan He, Hongtao Xie, Tong Guo, Dawei Yin, and Yongdong Zhang. "Semi-supervised User Profiling with Heterogeneous Graph Attention Networks." 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.2019/293.

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Aiming to represent user characteristics and personal interests, the task of user profiling is playing an increasingly important role for many real-world applications, e.g., e-commerce and social networks platforms. By exploiting the data like texts and user behaviors, most existing solutions address user profiling as a classification task, where each user is formulated as an individual data instance. Nevertheless, a user's profile is not only reflected from her/his affiliated data, but also can be inferred from other users, e.g., the users that have similar co-purchase behaviors in e-commerce, the friends in social networks, etc. In this paper, we approach user profiling in a semi-supervised manner, developing a generic solution based on heterogeneous graph learning. On the graph, nodes represent the entities of interest (e.g., users, items, attributes of items, etc.), and edges represent the interactions between entities. Our heterogeneous graph attention networks (HGAT) method learns the representation for each entity by accounting for the graph structure, and exploits the attention mechanism to discriminate the importance of each neighbor entity. Through such a learning scheme, HGAT can leverage both unsupervised information and limited labels of users to build the predictor. Extensive experiments on a real-world e-commerce dataset verify the effectiveness and rationality of our HGAT for user profiling.
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Xiu, Yuhuan, Man Lan, Yuanbin Wu, and Jun Lang. "Exploring semantic content to user profiling for user cluster-based collaborative point-of-interest recommender system." In 2017 International Conference on Asian Language Processing (IALP). IEEE, 2017. http://dx.doi.org/10.1109/ialp.2017.8300595.

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Shtykh, Roman Y., and Qun Jin. "Enhancing IR with User-Centric Integrated Approach of Interest Change Driven Layered Profiling and User Contributions." In 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07). IEEE, 2007. http://dx.doi.org/10.1109/ainaw.2007.175.

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Ahmed, Ghada, and Fatma Meawed. "Topical User Profiling from Twitter for Point of Interest (POI) Recommendation in an Augmented Reality View." In Annual International Conference on Computer Games Multimedia and Allied Technologies (CGAT 2017). Global Science & Technology Forum (GSTF), 2017. http://dx.doi.org/10.5176/2251-1679_cgat17.7.

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Hu, Renjun, Xinjiang Lu, Chuanren Liu, Yanyan Li, Hao Liu, Jingjing Gu, Shuai Ma, and Hui Xiong. "Why We Go Where We Go: Profiling User Decisions on Choosing POIs." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/478.

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While Point-of-Interest (POI) recommendation has been a popular topic of study for some time, little progress has been made for understanding why and how people make their decisions for the selection of POIs. To this end, in this paper, we propose a user decision profiling framework, named PROUD, which can identify the key factors in people's decisions on choosing POIs. Specifically, we treat each user decision as a set of factors and provide a method for learning factor embeddings. A unique perspective of our approach is to identify key factors, while preserving decision structures seamlessly, via a novel scalar projection maximization objective. Exactly solving the objective is non-trivial due to a sparsity constraint. To address this, our PROUD adopts a self projection attention and an L2 regularized sparse activation to directly estimate the likelihood of each factor to be a key factor. Finally, extensive experiments on real-world data validate the advantage of PROUD in preserving user decision structures. Also, our case study indicates that the identified key decision factors can help us to provide more interpretable recommendations and analyses.
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Reports on the topic "User Interest Profiling"

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Sessa, Guido, and Gregory Martin. MAP kinase cascades activated by SlMAPKKKε and their involvement in tomato resistance to bacterial pathogens. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7699834.bard.

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The research problem: Pseudomonas syringae pv. tomato (Pst) and Xanthomonas campestrispv. vesicatoria (Xcv) are the causal agents of tomato bacterial speck and spot diseases, respectively. These pathogens colonize the aerial parts of the plant and cause economically important losses to tomato yield worldwide. Control of speck and spot diseases by cultural practices or chemicals is not effective and genetic sources of resistance are very limited. In previous research supported by BARD, by gene expression profiling we identified signaling components involved in resistance to Xcvstrains. Follow up experiments revealed that a tomato gene encoding a MAP kinase kinase kinase (MAPKKKe) is required for resistance to Xcvand Pststrains. Goals: Central goal of this research was to investigate the molecular mechanisms by which MAPKKKεand associated MAP kinase cascades regulate host resistance. Specific objectives were to: 1. Determine whether MAPKKKεplays a broad role in defense signaling in plants; 2. Identify components of MAP kinase cascades acting downstream of MAPKKKε; 3. Determine the role of phosphorylation-related events in the function of MAPKKKε; 4. Isolate proteins directly activated by MAPKKKε-associatedMAPK modules. Our main achievements during this research program are in the following major areas: 1. Characterization of MAPKKKεas a positive regulator of cell death and dissection of downstream MAP kinase cascades (Melech-Bonfil et al., 2010; Melech-Bonfil and Sessa, 2011). The MAPKKKεgene was found to be required for tomato resistance to Xcvand Pstbacterial strains and for hypersensitive response cell death triggered by different R gene/effector gene pairs. In addition, overexpression analysis demonstrated that MAPKKKεis a positive regulator of cell death, whose activity depends on an intact kinase catalytic domain. Epistatic experiments delineated a signaling cascade downstream of MAPKKKεand identified SIPKK as a negative regulator of MAPKKKε-mediated cell death. Finally, genes encoding MAP kinase components downstream of MAPKKKεwere shown to contribute to tomato resistance to Xcv. 2. Identification of tomato proteins that interact with MAPKKKεand play a role in plant immunity (Oh et al., 2011). We identified proteins that interact with MAPKKKε. Among them, the 14-3-3 protein TFT7 was required for cell death mediated by several R proteins. In addition, TFT7 interacted with the MAPKK SlMKK2 and formed homodimersin vivo. Thus, TFT7 is proposed to recruit SlMKK2 and MAPKKK client proteins for efficient signal transfer. 3. Development of a chemical genetic approach to identify substrates of MAPKKKε-activated MAP kinase cascades (Salomon et al., 2009, 2011). This approach is based on engineering the kinase of interest to accept unnatural ATP analogs. For its implementation to identify substrates of MAPKKKε-activated MAP kinase modules, we sensitized the tomato MAP kinase SlMPK3 to ATP analogs and verified its ability to use them as phosphodonors. By using the sensitized SlMPK3 and radiolabeled N6(benzyl)ATP it should be possible to tag direct substrates of this kinase. 4. Development of methods to study immunity triggered by pathogen-associated molecular patterns (PAMPs) in tomato and N. benthamiana plants (Kim et al., 2009; Nguyen et al. 2010). We developed protocols for measuring various PTI-associatedphenotypes, including bacterial populations after pretreatment of leaves with PAMPs, induction of reporter genes, callose deposition at the cell wall, activation of MAP kinases, and a luciferase-based reporter system for use in protoplasts. Scientific and agricultural significance: Our research activities discovered and characterized a signal transduction pathway mediating plant immunity to bacterial pathogens. Increased understanding of molecular mechanisms of immunity will allow them to be manipulated by both molecular breeding and genetic engineering to produce plants with enhanced natural defense against disease. In addition, we successfully developed new biochemical and molecular methods that can be implemented in the study of plant immunity and other aspects of plant biology.
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