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Статті в журналах з теми "Neural Cross-Domain Collaborative Filtering"
Yang, Dong, and Jian Sun. "BM3D-Net: A Convolutional Neural Network for Transform-Domain Collaborative Filtering." IEEE Signal Processing Letters 25, no. 1 (January 2018): 55–59. http://dx.doi.org/10.1109/lsp.2017.2768660.
Повний текст джерелаWang, Jiahao, Hongyan Mei, Kai Li, Xing Zhang, and Xin Chen. "Collaborative Filtering Model of Graph Neural Network Based on Random Walk." Applied Sciences 13, no. 3 (January 30, 2023): 1786. http://dx.doi.org/10.3390/app13031786.
Повний текст джерелаAlaa El-deen Ahmed, Rana, Manuel Fernández-Veiga, and Mariam Gawich. "Neural Collaborative Filtering with Ontologies for Integrated Recommendation Systems." Sensors 22, no. 2 (January 17, 2022): 700. http://dx.doi.org/10.3390/s22020700.
Повний текст джерелаWójcik, Filip, and Michał Górnik. "Improvement of e-commerce recommendation systems with deep hybrid collaborative filtering with content: A case study." Econometrics 24, no. 3 (2020): 37–50. http://dx.doi.org/10.15611/eada.2020.3.03.
Повний текст джерелаFeng, Ying, and Guisheng Zhao. "Implementation of Short Video Click-Through Rate Estimation Model Based on Cross-Media Collaborative Filtering Neural Network." Computational Intelligence and Neuroscience 2022 (May 31, 2022): 1–13. http://dx.doi.org/10.1155/2022/4951912.
Повний текст джерелаWang, Li, and Cheng Zhong. "Prediction of miRNA-Disease Association Using Deep Collaborative Filtering." BioMed Research International 2021 (February 24, 2021): 1–16. http://dx.doi.org/10.1155/2021/6652948.
Повний текст джерелаSahoo, Abhaya Kumar, Chittaranjan Pradhan, Rabindra Kumar Barik, and Harishchandra Dubey. "DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering." Computation 7, no. 2 (May 22, 2019): 25. http://dx.doi.org/10.3390/computation7020025.
Повний текст джерелаSethuraman, Ram, and Akshay Havalgi. "Novel Approach of Neural Collaborative Filter by Pairwise Objective Function with Matrix Factorization." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 1213. http://dx.doi.org/10.14419/ijet.v7i3.12.17840.
Повний текст джерелаSyed, Muzamil Hussain, Tran Quoc Bao Huy, and Sun-Tae Chung. "Context-Aware Explainable Recommendation Based on Domain Knowledge Graph." Big Data and Cognitive Computing 6, no. 1 (January 20, 2022): 11. http://dx.doi.org/10.3390/bdcc6010011.
Повний текст джерелаLu, Jing. "Personalized Recommendation Algorithm of Smart Tourism Based on Cross-Media Big Data and Neural Network." Computational Intelligence and Neuroscience 2022 (June 26, 2022): 1–11. http://dx.doi.org/10.1155/2022/9566766.
Повний текст джерелаДисертації з теми "Neural Cross-Domain Collaborative Filtering"
Parimi, Rohit. "Collaborative filtering approaches for single-domain and cross-domain recommender systems." Diss., Kansas State University, 2015. http://hdl.handle.net/2097/20108.
Повний текст джерелаComputing and Information Sciences
Doina Caragea
Increasing amounts of content on the Web means that users can select from a wide variety of items (i.e., items that concur with their tastes and requirements). The generation of personalized item suggestions to users has become a crucial functionality for many web applications as users benefit from being shown only items of potential interest to them. One popular solution to creating personalized item suggestions to users is recommender systems. Recommender systems can address the item recommendation task by utilizing past user preferences for items captured as either explicit or implicit user feedback. Numerous collaborative filtering (CF) approaches have been proposed in the literature to address the recommendation problem in the single-domain setting (user preferences from only one domain are used to recommend items). However, increasingly large datasets often prevent experimentation of every approach in order to choose the one that best fits an application domain. The work in this dissertation on the single-domain setting studies two CF algorithms, Adsorption and Matrix Factorization (MF), considered to be state-of-the-art approaches for implicit feedback and suggests that characteristics of a domain (e.g., close connections versus loose connections among users) or characteristics of data available (e.g., density of the feedback matrix) can be useful in selecting the most suitable CF approach to use for a particular recommendation problem. Furthermore, for Adsorption, a neighborhood-based approach, this work studies several ways to construct user neighborhoods based on similarity functions and on community detection approaches, and suggests that domain and data characteristics can also be useful in selecting the neighborhood approach to use for Adsorption. Finally, motivated by the need to decrease computational costs of recommendation algorithms, this work studies the effectiveness of using short-user histories and suggests that short-user histories can successfully replace long-user histories for recommendation tasks. Although most approaches for recommender systems use user preferences from only one domain, in many applications, user interests span items of various types (e.g., artists and tags). Each recommendation problem (e.g., recommending artists to users or recommending tags to users) can be considered unique domains, and user preferences from several domains can be used to improve accuracy in one domain, an area of research known as cross-domain recommender systems. The work in this dissertation on cross-domain recommender systems investigates several limitations of existing approaches and proposes three novel approaches (two Adsorption-based and one MF-based) to improve recommendation accuracy in one domain by leveraging knowledge from multiple domains with implicit feedback. The first approach performs aggregation of neighborhoods (WAN) from the source and target domains, and the neighborhoods are used with Adsorption to recommend target items. The second approach performs aggregation of target recommendations (WAR) from Adsorption computed using neighborhoods from the source and target domains. The third approach integrates latent user factors from source domains into the target through a regularized latent factor model (CIMF). Experimental results on six target recommendation tasks from two real-world applications suggest that the proposed approaches effectively improve target recommendation accuracy as compared to single-domain CF approaches and successfully utilize varying amounts of user overlap between source and target domains. Furthermore, under the assumption that tuning may not be possible for large recommendation problems, this work proposes an approach to calculate knowledge aggregation weights based on network alignment for WAN and WAR approaches, and results show the usefulness of the proposed solution. The results also suggest that the WAN and WAR approaches effectively address the cold-start user problem in the target domain.
SILVA, Douglas Véras e. "CD-cars: cross domain context-aware recomender systems." Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/18356.
Повний текст джерелаMade available in DSpace on 2017-02-21T16:47:42Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) dvsTeseBiblioteca.pdf: 6571192 bytes, checksum: eb7914e5ffef25b8f01ff92d9a60c164 (MD5) Previous issue date: 2016-07-21
FACEPE
Traditionally, single-domain recommender systems (SDRS) have achieved good results in recommending relevant items for users in order to solve the information overload problem. However, cross-domain recommender systems (CDRS) have emerged aiming to enhance SDRS by achieving some goals such as accuracy improvement, diversity, addressing new user and new item problems, among others. Instead of treating each domain independently, CDRS use knowledge acquired in a source domain (e.g. books) to improve the recommendation in a target domain (e.g. movies). Likewise SDRS research, collaborative filtering (CF) is considered the most popular and widely adopted approach in CDRS, because its implementation for any domain is relatively simple. In addition, its quality of recommendation is usually higher than that of content-based filtering (CBF) algorithms. In fact, the majority of the cross-domain collaborative filtering RS (CD-CFRS) can give better recommendations in comparison to single domain collaborative filtering recommender systems (SD-CFRS), leading to a higher users’ satisfaction and addressing cold-start, sparsity, and diversity problems. However, CD-CFRS may not necessarily be more accurate than SD-CFRS. On the other hand, context-aware recommender systems (CARS) deal with another relevant topic of research in the recommender systems area, aiming to improve the quality of recommendations too. Different contextual information (e.g., location, time, mood, etc.) can be leveraged in order to provide recommendations that are more suitable and accurate for a user depending on his/her context. In this way, we believe that the integration of techniques developed in isolation (cross-domain and contextaware) can be useful in a variety of situations, in which recommendations can be improved by information from different sources as well as they can be refined by considering specific contextual information. In this thesis, we define a novel formulation of the recommendation problem, considering both the availability of information from different domains (source and target) and the use of contextual information. Based on this formulation, we propose the integration of cross-domain and context-aware approaches for a novel recommender system (CD-CARS). To evaluate the proposed CD-CARS, we performed experimental evaluations through two real datasets with three different contextual dimensions and three distinct domains. The results of these evaluations have showed that the use of context-aware techniques can be considered as a good approach in order to improve the cross-domain recommendation quality in comparison to traditional CD-CFRS.
Tradicionalmente, “sistemas de recomendação de domínio único” (SDRS) têm alcançado bons resultados na recomendação de itens relevantes para usuários, a fim de resolver o problema da sobrecarga de informação. Entretanto, “sistemas de recomendação de domínio cruzado” (CDRS) têm surgido visando melhorar os SDRS ao atingir alguns objetivos, tais como: “melhoria de precisão”, “melhor diversidade”, abordar os problemas de “novo usuário” e “novo item”, dentre outros. Ao invés de tratar cada domínio independentemente, CDRS usam conhecimento adquirido em um domínio fonte (e.g. livros) a fim de melhorar a recomendação em um domínio alvo (e.g. filmes). Assim como acontece na área de pesquisa sobre SDRS, a filtragem colaborativa (CF) é considerada a técnica mais popular e amplamente utilizada em CDRS, pois sua implementação para qualquer domínio é relativamente simples. Além disso, sua qualidade de recomendação é geralmente maior do que a dos algoritmos baseados em filtragem de conteúdo (CBF). De fato, a maioria dos “sistemas de recomendação de domínio cruzado” baseados em filtragem colaborativa (CD-CFRS) podem oferecer melhores recomendações em comparação a “sistemas de recomendação de domínio único” baseados em filtragem colaborativa (SD-CFRS), aumentando o nível de satisfação dos usuários e abordando problemas tais como: “início frio”, “esparsidade” e “diversidade”. Entretanto, os CD-CFRS podem não ser mais precisos do que os SD-CFRS. Por outro lado, “sistemas de recomendação sensíveis à contexto” (CARS) tratam de outro tópico relevante na área de pesquisa de sistemas de recomendação, também visando melhorar a qualidade das recomendações. Diferentes informações contextuais (e.g. localização, tempo, humor, etc.) podem ser utilizados a fim de prover recomendações que são mais adequadas e precisas para um usuário dependendo de seu contexto. Desta forma, nós acreditamos que a integração de técnicas desenvolvidas separadamente (de “domínio cruzado” e “sensíveis a contexto”) podem ser úteis em uma variedade de situações, nas quais as recomendações podem ser melhoradas a partir de informações obtidas em diferentes fontes além de refinadas considerando informações contextuais específicas. Nesta tese, nós definimos uma nova formulação do problema de recomendação, considerando tanto a disponibilidade de informações de diferentes domínios (fonte e alvo) quanto o uso de informações contextuais. Baseado nessa formulação, nós propomos a integração de abordagens de “domínio cruzado” e “sensíveis a contexto” para um novo sistema de recomendação (CD-CARS). Para avaliar o CD-CARS proposto, nós realizamos avaliações experimentais através de dois “conjuntos de dados” com três diferentes dimensões contextuais e três domínios distintos. Os resultados dessas avaliações mostraram que o uso de técnicas sensíveis a contexto pode ser considerado como uma boa abordagem a fim de melhorar a qualidade de recomendações de “domínio cruzado” em comparação às recomendações de CD-CFRS tradicionais.
Alharthi, Haifa. "The Use of Items Personality Profiles in Recommender Systems." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/31922.
Повний текст джерелаVijaikumar, M. "Neural Models for Personalized Recommendation Systems with External Information." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5667.
Повний текст джерелаLiu, Yan Fu, and 劉彥甫. "Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/45693782400620833091.
Повний текст джерела國立清華大學
資訊工程學系
103
The Cross Domain Collaborative Filtering (CDCF) exploits the rating matrices from multiple domains to make better recommendations. Existing CDCF methods adopt the sub-structure sharing technique that can only transfer linearly correlated knowledge between domains. In this paper, we propose the notion of Hyper-Structure Transfer (HST) that requires the rating matrices to be explained by the projections of some more complex structure, called the hyper-structure, shared by all domains, and thus allows the non-linearly correlated knowledge between domains to be identified and transferred. Extensive experiments are conducted and the results demonstrate the effectiveness of our HST models empirically.
Частини книг з теми "Neural Cross-Domain Collaborative Filtering"
Vijaikumar, M., Shirish Shevade, and M. N. Murty. "Neural Cross-Domain Collaborative Filtering with Shared Entities." In Machine Learning and Knowledge Discovery in Databases, 729–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67658-2_42.
Повний текст джерелаZhang, Zhigao, Jing Qin, Feng Li, and Bin Wang. "Cross Product and Attention Based Deep Neural Collaborative Filtering." In Advanced Data Mining and Applications, 453–61. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65390-3_35.
Повний текст джерелаTiroshi, Amit, and Tsvi Kuflik. "Domain Ranking for Cross Domain Collaborative Filtering." In User Modeling, Adaptation, and Personalization, 328–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31454-4_30.
Повний текст джерелаLoni, Babak, Yue Shi, Martha Larson, and Alan Hanjalic. "Cross-Domain Collaborative Filtering with Factorization Machines." In Lecture Notes in Computer Science, 656–61. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06028-6_72.
Повний текст джерелаVijaikumar, M., Shirish Shevade, and M. N. Murty. "TagEmbedSVD: Leveraging Tag Embeddings for Cross-Domain Collaborative Filtering." In Lecture Notes in Computer Science, 240–48. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34872-4_27.
Повний текст джерелаEnrich, Manuel, Matthias Braunhofer, and Francesco Ricci. "Cold-Start Management with Cross-Domain Collaborative Filtering and Tags." In Lecture Notes in Business Information Processing, 101–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39878-0_10.
Повний текст джерелаYu, Xu, Feng Jiang, Miao Yu, and Ying Guo. "Cross Domain Collaborative Filtering by Integrating User Latent Vectors of Auxiliary Domains." In Knowledge Science, Engineering and Management, 334–45. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63558-3_28.
Повний текст джерелаChang, Jiaqi, Fusheng Yu, and Huanan Pu. "Fusing Information by Knowledge-Guidance Based Clustering in Cross-Domain Collaborative Filtering." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 1800–1807. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70665-4_194.
Повний текст джерелаFernández-Tobías, Ignacio, and Iván Cantador. "On the Use of Cross-Domain User Preferences and Personality Traits in Collaborative Filtering." In Lecture Notes in Computer Science, 343–49. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20267-9_29.
Повний текст джерелаShi, Yue, Martha Larson, and Alan Hanjalic. "Tags as Bridges between Domains: Improving Recommendation with Tag-Induced Cross-Domain Collaborative Filtering." In User Modeling, Adaption and Personalization, 305–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22362-4_26.
Повний текст джерелаТези доповідей конференцій з теми "Neural Cross-Domain Collaborative Filtering"
Liu, Meng, Jianjun Li, Guohui Li, Zhiqiang Guo, Chaoyang Wang, and Peng Pan. "Cross Domain Deep Collaborative Filtering without Overlapping Data." In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10191115.
Повний текст джерелаKang, Yachen, Sibo Gai, Feng Zhao, Donglin Wang, and Ao Tang. "Deep Transfer Collaborative Filtering with Geometric Structure Preservation for Cross-Domain Recommendation." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207009.
Повний текст джерелаKang, Yachen, Sibo Gai, Feng Zhao, Donglin Wang, and Yi Luo. "Cross-Domain Deep Collaborative Filtering for Recommendation." In 2019 International Conference on Data Mining Workshops (ICDMW). IEEE, 2019. http://dx.doi.org/10.1109/icdmw.2019.00096.
Повний текст джерелаDoan, Thanh-Nam, and Shaghayegh Sahebi. "TransCrossCF: Transition-based Cross-Domain Collaborative Filtering." In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2020. http://dx.doi.org/10.1109/icmla51294.2020.00059.
Повний текст джерелаLi, Bin. "Cross-Domain Collaborative Filtering: A Brief Survey." In 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2011. http://dx.doi.org/10.1109/ictai.2011.184.
Повний текст джерелаJin, Di, Luzhi Wang, Yizhen Zheng, Xiang Li, Fei Jiang, Wei Lin, and Shirui Pan. "CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/292.
Повний текст джерелаGuo, Yunhui, Xin Wang, and Congfu Xu. "CroRank: Cross Domain Personalized Transfer Ranking for Collaborative Filtering." In 2015 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE, 2015. http://dx.doi.org/10.1109/icdmw.2015.46.
Повний текст джерелаZang, Yizhou, and Xiaohua Hu. "Heterogeneous knowledge transfer via domain regularization for improving cross-domain collaborative filtering." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258407.
Повний текст джерелаWang, Weiqing, Zhenyu Chen, Jia Liu, Qi Qi, and Zhihong Zhao. "User-based collaborative filtering on cross domain by tag transfer learning." In the 1st International Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2351333.2351335.
Повний текст джерелаLiu, Meng, Jianjun Li, Guohui Li, and Peng Pan. "Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks." In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3340531.3412012.
Повний текст джерела