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Статті в журналах з теми "Semantic Recommender"
Hendrik, Khamidudin Azzakiy, and Aditya Budi Utomo. "Semantic Hybrid Recommender System." Advanced Science Letters 21, no. 10 (October 1, 2015): 3363–66. http://dx.doi.org/10.1166/asl.2015.6499.
Повний текст джерелаSulieman, Dalia, Maria Malek, Hubert Kadima, and Dominique Laurent. "Toward Social-Semantic Recommender Systems." International Journal of Information Systems and Social Change 7, no. 1 (January 2016): 1–30. http://dx.doi.org/10.4018/ijissc.2016010101.
Повний текст джерелаFraihat, Salam, and Qusai Shambour. "A Framework of Semantic Recommender System for e-Learning." Journal of Software 10, no. 3 (March 2015): 317–30. http://dx.doi.org/10.17706/jsw.10.3.317-330.
Повний текст джерелаCantador, Iván, Pablo Castells, and Alejandro Bellogín. "An Enhanced Semantic Layer for Hybrid Recommender Systems." International Journal on Semantic Web and Information Systems 7, no. 1 (January 2011): 44–78. http://dx.doi.org/10.4018/jswis.2011010103.
Повний текст джерелаHawashin, Bilal, Shadi Alzubi, Tarek Kanan, and Ayman Mansour. "An efficient semantic recommender method forArabic text." Electronic Library 37, no. 2 (April 1, 2019): 263–80. http://dx.doi.org/10.1108/el-12-2018-0245.
Повний текст джерелаJaglan, G., and S. K. Malik. "Blending Semantic Web with Recommender Systems." International Journal of Computer Sciences and Engineering 6, no. 5 (May 31, 2018): 523–31. http://dx.doi.org/10.26438/ijcse/v6i5.523531.
Повний текст джерелаVellino, Andre. "Recommending research articles using citation data." Library Hi Tech 33, no. 4 (November 16, 2015): 597–609. http://dx.doi.org/10.1108/lht-06-2015-0063.
Повний текст джерелаDurao, Frederico, and Peter Dolog. "Semantic Grounding Strategies for Tagbased Recommender Systems." International journal of Web & Semantic Technology 2, no. 4 (October 30, 2011): 67–79. http://dx.doi.org/10.5121/ijwest.2011.2405.
Повний текст джерелаGarcía-Sánchez, Francisco, Ricardo Colomo-Palacios, and Rafael Valencia-García. "A social-semantic recommender system for advertisements." Information Processing & Management 57, no. 2 (March 2020): 102153. http://dx.doi.org/10.1016/j.ipm.2019.102153.
Повний текст джерелаShu, Qiong, He Ping Chen, and Jin Guang Gu. "Semantic Reasoning-Based Chinese Recipe Recommender System." Advanced Materials Research 718-720 (July 2013): 1998–2004. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1998.
Повний текст джерелаДисертації з теми "Semantic Recommender"
Sulieman, Dalia. "Towards Semantic-Social Recommender Systems." Phd thesis, Université de Cergy Pontoise, 2014. http://tel.archives-ouvertes.fr/tel-01017586.
Повний текст джерелаMENDONCA, DIOGO SILVEIRA. "PROBABILISTIC LATENT SEMANTIC ANALYSIS APPLIED TO RECOMMENDER SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=13073@1.
Повний текст джерелаRecommender systems are a constant research topic because of their large number of practical applications. There are many approaches to address these problems, one of the most widely used being collaborative filtering, in which in order to recommend an item to a user, data of other users` behaviors are employed. However, collaborative filtering algorithms do not always reach levels of precision required for the use in real applications. Within this context, the present work aims to evaluate the performance of the probabilistic latent semantic analysis (PLSA) applied to recommender systems. This model identifies groups of users with similar behaviors through latent attributes, allowing the use of these behaviors in the recommendation. To check the effectiveness of the method, there were presented experiments with problems of both web ad recommending and film recommending. An improvement of 18,7% were found in the accuracy of the recommendation of ads on the web and we also found 3.7% of improvement in Root Mean Square Error over the Means of Means baseline system for the Netflix corpus. Apart from the aforementioned experiments, the algorithm was implemented in a flexible and reusable way, allowing its adaptation to other problems with reduced effort. This implementation has also been incorporated as a module of LearnAds, a framework for the recommendation of ads on the web.
Garden, Matthew. "On the use of semantic feedback in recommender systems." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82237.
Повний текст джерелаWe have developed an implementation of our approach, and have collected extensive empirical data based on movie ratings. We demonstrate the effectiveness of our approach, and describe the details of the implementation.
Borràs, Nogués Joan. "Semantic recommender systems Provision of personalised information about tourist activities." Doctoral thesis, Universitat Rovira i Virgili, 2015. http://hdl.handle.net/10803/310219.
Повний текст джерелаEsta tesis estudia cómo mejorar los sistemas de recomendación utilizando información semántica sobre un determinado dominio, en el caso de este trabajo el Turismo. Las ontologías definen un conjunto de conceptos relacionados con un determinado dominio, así como las relaciones entre ellos. East estructuras de conocimiento pueden ser utilizadas no sólo para representar de una manera más precisa y refinada los objetos del dominio y las preferencias de los usuarios, sino también para aplicar mejor los procedimientos de comparación entre los objetos y usuarios (y también entre los propios usuarios) con la ayuda de medidas de similitud semántica. Las mejoras al nivel de la representación del conocimiento y al nivel de razonamiento conducen a recomendaciones más precisas y a una mejora del rendimiento de los sistemas de recomendación, generando nuevos sistemas de recomendación semánticos inteligentes. Las dos técnicas de recomendación básicas, basadas en contenido y en filtrado colaborativo, se benefician de la introducción de conocimiento explícito del dominio. En esta tesis también hemos diseñado y desarrollado un sistema de recomendación que aplica los métodos que hemos propuesto. Este recomendador está diseñado para proporcionar recomendaciones personalizadas sobre las actividades turísticas en la región de Tarragona. Las actividades están debidamente clasificadas y etiquetadas de acuerdo con una ontología específica, que guía el proceso de razonamiento. El recomendador tiene en cuenta diferentes tipos de datos: información demográfica, las motivaciones de viaje, las acciones del usuario en el sistema, las calificaciones proporcionadas por el usuario, las opiniones de los usuarios con características demográficas similares o gustos similares, etc. Un proceso de diversificación que calcula similitudes entre objetos se aplica para generar variedad en las recomendaciones y por tanto aumentar la satisfacción del usuario. Este sistema puede tener un impacto positivo en la región al mejorar la experiencia de sus visitantes.
This dissertation studies how new improvements can be made on recommender systems by using ontological information about a certain domain (in the case of this work, Tourism). Ontologies define a set of concepts related to a certain domain as well as the relationships among them. These knowledge structures may be used not only to represent in a more precise and refined way the domain objects and the user preferences, but also to apply better matching procedures between objects and users (or between users themselves) with the help of semantic similarity measures. The improvements at the knowledge representation level and at the reasoning level lead to more accurate recommendations and to an improvement of the performance of recommender systems, paving the way towards a new generation of smart semantic recommender systems. Both content-based recommendation techniques and collaborative filtering ones certainly benefit from the introduction of explicit domain knowledge. In this thesis we have also designed and developed a recommender system that applies the methods we have proposed. This recommender is designed to provide personalized recommendations of touristic activities in the region of Tarragona. The activities are properly classified and labelled according to a specific ontology, which guides the reasoning process. The recommender takes into account many different kinds of data: demographic information, travel motivations, the actions of the user on the system, the ratings provided by the user, the opinions of users with similar demographic characteristics or similar tastes, etc. A diversification process that computes similarities between objects is applied to produce diverse recommendations and hence increase user satisfaction. This system can have a beneficial impact on the region by improving the experience of its visitors.
Eryol, Erkin. "Probabilistic Latent Semantic Analysis Based Framework For Hybrid Social Recommender Systems." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/2/12611921/index.pdf.
Повний текст джерелаJoseph, Daniel. "Linking information resources with automatic semantic extraction." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/linking-information-resources-with-automatic-semantic-extraction(ada2db36-4366-441a-a0a9-d76324a77e2c).html.
Повний текст джерелаAkther, Aysha. "Social Tag-based Community Recommendation Using Latent Semantic Analysis." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23238.
Повний текст джерелаFIGUEROA, MARTINEZ CRISTHIAN NICOLAS. "Recommender Systems based on Linked Data." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2669963.
Повний текст джерелаPALUMBO, ENRICO. "Knowledge Graph Embeddings for Recommender Systems." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850588.
Повний текст джерелаSundaramurthy, Roshni. "Recommender System for Gym Customers." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166147.
Повний текст джерелаЧастини книг з теми "Semantic Recommender"
Ziegler, Cai-Nicolas. "Semantic Web Recommender Systems." In Current Trends in Database Technology - EDBT 2004 Workshops, 78–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30192-9_8.
Повний текст джерелаGedikli, Fatih, and Dietmar Jannach. "Recommender Systems, Semantic-Based." In Encyclopedia of Social Network Analysis and Mining, 1–11. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_116-1.
Повний текст джерелаGedikli, Fatih, and Dietmar Jannach. "Recommender Systems, Semantic-Based." In Encyclopedia of Social Network Analysis and Mining, 1501–10. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_116.
Повний текст джерелаGedikli, Fatih, and Dietmar Jannach. "Recommender Systems, Semantic-Based." In Encyclopedia of Social Network Analysis and Mining, 2137–47. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_116.
Повний текст джерелаKonstan, Joseph A., and John T. Riedl. "Recommender Systems for the Web." In Visualizing the Semantic Web, 151–67. London: Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-3737-5_10.
Повний текст джерелаMaccatrozzo, Valentina, Davide Ceolin, Lora Aroyo, and Paul Groth. "A Semantic Pattern-Based Recommender." In Communications in Computer and Information Science, 182–87. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12024-9_24.
Повний текст джерелаMirizzi, Roberto, Tommaso Di Noia, Eugenio Di Sciascio, and Azzurra Ragone. "A Recommender System for Linked Data." In Semantic Search over the Web, 311–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25008-8_12.
Повний текст джерелаBaba-Hamed, Latifa, and Magloire Namber. "Diversity in a Semantic Recommender System." In Advances in Intelligent Systems and Computing, 297–306. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01863-8_32.
Повний текст джерелаThanh-Tai, Huynh, Huu-Hoa Nguyen, and Nguyen Thai-Nghe. "A Semantic Approach in Recommender Systems." In Future Data and Security Engineering, 331–43. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48057-2_23.
Повний текст джерелаAnelli, Vito Walter, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, and Felice Antonio Merra. "SAShA: Semantic-Aware Shilling Attacks on Recommender Systems Exploiting Knowledge Graphs." In The Semantic Web, 307–23. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49461-2_18.
Повний текст джерелаТези доповідей конференцій з теми "Semantic Recommender"
Wang, Rui-Qin, and Fan-Sheng Kong. "Semantic-Enhanced Personalized Recommender System." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370858.
Повний текст джерелаNeirynck, Thomas. "Semantic road networks for recommender systems." In the International Working Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2254556.2254691.
Повний текст джерелаGe, Mouzhi, and Fabio Persia. "Research Challenges in Multimedia Recommender Systems." In 2017 IEEE 11th International Conference on Semantic Computing (ICSC). IEEE, 2017. http://dx.doi.org/10.1109/icsc.2017.31.
Повний текст джерелаLémdani, Roza, Géraldine Polaillon, Nacéra Bennacer, and Yolaine Bourda. "A semantic similarity measure for recommender systems." In the 7th International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2063518.2063545.
Повний текст джерелаZaremarjal, Ashkan Yeganeh, and Derya Yiltas-Kaplan. "Semantic Collaborative Filtering Recommender System Using CNNs." In 2021 8th International Conference on Electrical and Electronics Engineering (ICEEE). IEEE, 2021. http://dx.doi.org/10.1109/iceee52452.2021.9415931.
Повний текст джерелаBafna, Prafulla, Shailaja Shirwaikar, and Dhanya Pramod. "Semantic Clustering Driven Approaches to Recommender Systems." In ACM COMPUTE '16: Ninth Annual ACM India Conference. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2998476.2998487.
Повний текст джерелаFaralli, Stefano, Giovanni Stilo, and Paola Velardi. "A Semantic Recommender for Micro-blog Users." In 2015 IEEE International Congress on Big Data (BigData Congress). IEEE, 2015. http://dx.doi.org/10.1109/bigdatacongress.2015.18.
Повний текст джерелаRakow, Christian, Andreas Lommatzsch, and Till Plumbaum. "Topical Semantic Recommendations for Auteur Films." In RecSys '16: Tenth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2959100.2959110.
Повний текст джерелаHou, Min, Le Wu, Enhong Chen, Zhi Li, Vincent W. Zheng, and Qi Liu. "Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach." 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.2019/650.
Повний текст джерелаVogiatzis, Dimitrios, and Nicolas Tsapatsoulis. "Modeling User Networks in Recommender Systems." In 2008 Third International Workshop on Semantic Media Adaptation and Personalization (SMAP). IEEE, 2008. http://dx.doi.org/10.1109/smap.2008.35.
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