FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3198323323> ?p ?o ?g. }

• W3198323323 endingPage "327" @default.
• W3198323323 startingPage "319" @default.
• W3198323323 abstract "Khan, Aslam Hasan Siddqui, Jamshed Sohail, Shahab SaquibA recommender system is considered to provide user-specific recommendations of products or any kind of services depending upon different sectors such as business, marketing, government, education, health, etc. It is a tool or method that facilitates to manage any kind of online overloading problem between customers and organizations. Machine learning is being utilized to provide the intelligent recommender systems that are able to learn the behaviour based on historical data. A lot of work and related software based on machine learning have been proposed recently by using supervised and unsupervised learning in this area by using a collaborative, content-based and hybrid approach. But the existing work utilizes the supervised and unsupervised methods in proposing several recommender systems which are suffering from many shortcomings. The main disadvantage that we observe is that supervised and unsupervised learning techniques become incompatible and useless when data is dispersed and completely labelled data is not available. Therefore, recently a new concept of semi-supervised learning has been used to build recommender systems in various applications. In semi-supervised learning, little supervision is provided to unlabelled data by using available partial labels, which is also a cost-effective approach. Thus, it is necessary to provide a high-quality and instructive survey that reveals the importance of semi-supervised learning techniques along with collaborative content and hybrid filtering techniques, contributing to recommender systems. Hence, this paper presents a recent survey in the development of recommender systems." @default.
• W3198323323 created "2021-09-13" @default.
• W3198323323 creator A5011090870 @default.
• W3198323323 creator A5048270460 @default.
• W3198323323 creator A5057496674 @default.
• W3198323323 date "2021-08-29" @default.
• W3198323323 modified "2023-10-18" @default.
• W3198323323 title "A Survey of Recommender Systems Based on Semi-supervised Learning" @default.
• W3198323323 cites W1252856208 @default.
• W3198323323 cites W2030808931 @default.
• W3198323323 cites W2549064611 @default.
• W3198323323 cites W2592804878 @default.
• W3198323323 cites W2743289629 @default.
• W3198323323 cites W2803669957 @default.
• W3198323323 cites W2886405804 @default.
• W3198323323 cites W2887714298 @default.
• W3198323323 cites W2894232830 @default.
• W3198323323 cites W2895429976 @default.
• W3198323323 cites W2901054819 @default.
• W3198323323 cites W2950481684 @default.
• W3198323323 cites W2963929932 @default.
• W3198323323 cites W2966937748 @default.
• W3198323323 cites W2982574009 @default.
• W3198323323 cites W2984010107 @default.
• W3198323323 cites W2987219395 @default.
• W3198323323 cites W2995742387 @default.
• W3198323323 cites W2999624767 @default.
• W3198323323 cites W3003213456 @default.
• W3198323323 cites W3003595918 @default.
• W3198323323 cites W3007663754 @default.
• W3198323323 cites W3014972541 @default.
• W3198323323 cites W3020714689 @default.
• W3198323323 cites W3023905056 @default.
• W3198323323 cites W3026837853 @default.
• W3198323323 cites W3033735428 @default.
• W3198323323 cites W3042218399 @default.
• W3198323323 cites W3080236009 @default.
• W3198323323 cites W3084695837 @default.
• W3198323323 cites W3092161737 @default.
• W3198323323 cites W3097033950 @default.
• W3198323323 cites W3102143782 @default.
• W3198323323 cites W3200382369 @default.
• W3198323323 cites W4236294630 @default.
• W3198323323 doi "https://doi.org/10.1007/978-981-16-3071-2_27" @default.
• W3198323323 hasPublicationYear "2021" @default.
• W3198323323 type Work @default.
• W3198323323 sameAs 3198323323 @default.
• W3198323323 citedByCount "4" @default.
• W3198323323 countsByYear W31983233232022 @default.
• W3198323323 countsByYear W31983233232023 @default.
• W3198323323 crossrefType "book-chapter" @default.
• W3198323323 hasAuthorship W3198323323A5011090870 @default.
• W3198323323 hasAuthorship W3198323323A5048270460 @default.
• W3198323323 hasAuthorship W3198323323A5057496674 @default.
• W3198323323 hasConcept C119857082 @default.
• W3198323323 hasConcept C154945302 @default.
• W3198323323 hasConcept C23123220 @default.
• W3198323323 hasConcept C41008148 @default.
• W3198323323 hasConcept C557471498 @default.
• W3198323323 hasConceptScore W3198323323C119857082 @default.
• W3198323323 hasConceptScore W3198323323C154945302 @default.
• W3198323323 hasConceptScore W3198323323C23123220 @default.
• W3198323323 hasConceptScore W3198323323C41008148 @default.
• W3198323323 hasConceptScore W3198323323C557471498 @default.
• W3198323323 hasLocation W31983233231 @default.
• W3198323323 hasOpenAccess W3198323323 @default.
• W3198323323 hasPrimaryLocation W31983233231 @default.
• W3198323323 hasRelatedWork W2045871438 @default.
• W3198323323 hasRelatedWork W2316860054 @default.
• W3198323323 hasRelatedWork W2348159088 @default.
• W3198323323 hasRelatedWork W2350747448 @default.
• W3198323323 hasRelatedWork W2369936857 @default.
• W3198323323 hasRelatedWork W2499363748 @default.
• W3198323323 hasRelatedWork W2525876841 @default.
• W3198323323 hasRelatedWork W2576496345 @default.
• W3198323323 hasRelatedWork W2809363009 @default.
• W3198323323 hasRelatedWork W2968745142 @default.
• W3198323323 isParatext "false" @default.
• W3198323323 isRetracted "false" @default.
• W3198323323 magId "3198323323" @default.
• W3198323323 workType "book-chapter" @default.