Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313854540> ?p ?o ?g. }
Showing items 1 to 75 of
75
with 100 items per page.
- W4313854540 abstract "Heterogeneous Graph Neural Networks (GNNs) have shown good performance as a robust deep learning-based graph representation technique and have gained much research interest. Although it has adequately taken into account networks with a number of links and nodes, heterogeneity and the volume of semantic data provide significant obstacles. The attention mechanism, having great potential in a variety of areas, is one of the most interesting new developments in deep learning. This research demonstrates a system with two crucial attributes for embedding users and movies. The proposed framework achieves multi-level semantic attention using GNNs. We incorporated IMDB and Netflix Movie and TV Show datasets and merged them into a single consolidated dataset that was further utilized for results analysis. This paper mainly contributes a technique for movie recommendation using heterogeneous graphs and multi-level Semitics. We have proposed a framework that incorporates viewer and Director as an entity. During the research, we also combined two datasets in accordance with the proposed framework. After that, we evaluated the performance of the graph neural network on the heterogeneous graph. We discovered that the proposed model outperformed the current methodologies while using the proposed technique. Our model multilevel-Semitics-based framework shows effective results." @default.
- W4313854540 created "2023-01-10" @default.
- W4313854540 creator A5043915174 @default.
- W4313854540 creator A5053794277 @default.
- W4313854540 creator A5056314859 @default.
- W4313854540 creator A5074475005 @default.
- W4313854540 date "2022-12-10" @default.
- W4313854540 modified "2023-09-25" @default.
- W4313854540 title "Movie Recommender System Based On Heterogeneous Graph Neural Networks" @default.
- W4313854540 cites W103340358 @default.
- W4313854540 cites W1975563293 @default.
- W4313854540 cites W2049137142 @default.
- W4313854540 cites W2064675550 @default.
- W4313854540 cites W2142972908 @default.
- W4313854540 cites W2149288670 @default.
- W4313854540 cites W2605181250 @default.
- W4313854540 cites W2911286998 @default.
- W4313854540 cites W2950723285 @default.
- W4313854540 cites W2963919031 @default.
- W4313854540 cites W2965857891 @default.
- W4313854540 cites W3004507689 @default.
- W4313854540 cites W3045088573 @default.
- W4313854540 cites W3152335982 @default.
- W4313854540 cites W4285194161 @default.
- W4313854540 doi "https://doi.org/10.1109/icsai57119.2022.10005557" @default.
- W4313854540 hasPublicationYear "2022" @default.
- W4313854540 type Work @default.
- W4313854540 citedByCount "0" @default.
- W4313854540 crossrefType "proceedings-article" @default.
- W4313854540 hasAuthorship W4313854540A5043915174 @default.
- W4313854540 hasAuthorship W4313854540A5053794277 @default.
- W4313854540 hasAuthorship W4313854540A5056314859 @default.
- W4313854540 hasAuthorship W4313854540A5074475005 @default.
- W4313854540 hasConcept C108583219 @default.
- W4313854540 hasConcept C119857082 @default.
- W4313854540 hasConcept C124101348 @default.
- W4313854540 hasConcept C132525143 @default.
- W4313854540 hasConcept C136197465 @default.
- W4313854540 hasConcept C154945302 @default.
- W4313854540 hasConcept C2984842247 @default.
- W4313854540 hasConcept C41008148 @default.
- W4313854540 hasConcept C41608201 @default.
- W4313854540 hasConcept C50644808 @default.
- W4313854540 hasConcept C557471498 @default.
- W4313854540 hasConcept C75564084 @default.
- W4313854540 hasConcept C80444323 @default.
- W4313854540 hasConceptScore W4313854540C108583219 @default.
- W4313854540 hasConceptScore W4313854540C119857082 @default.
- W4313854540 hasConceptScore W4313854540C124101348 @default.
- W4313854540 hasConceptScore W4313854540C132525143 @default.
- W4313854540 hasConceptScore W4313854540C136197465 @default.
- W4313854540 hasConceptScore W4313854540C154945302 @default.
- W4313854540 hasConceptScore W4313854540C2984842247 @default.
- W4313854540 hasConceptScore W4313854540C41008148 @default.
- W4313854540 hasConceptScore W4313854540C41608201 @default.
- W4313854540 hasConceptScore W4313854540C50644808 @default.
- W4313854540 hasConceptScore W4313854540C557471498 @default.
- W4313854540 hasConceptScore W4313854540C75564084 @default.
- W4313854540 hasConceptScore W4313854540C80444323 @default.
- W4313854540 hasLocation W43138545401 @default.
- W4313854540 hasOpenAccess W4313854540 @default.
- W4313854540 hasPrimaryLocation W43138545401 @default.
- W4313854540 hasRelatedWork W2909645158 @default.
- W4313854540 hasRelatedWork W2950066684 @default.
- W4313854540 hasRelatedWork W3035116611 @default.
- W4313854540 hasRelatedWork W3082895349 @default.
- W4313854540 hasRelatedWork W3162132941 @default.
- W4313854540 hasRelatedWork W3179488938 @default.
- W4313854540 hasRelatedWork W4287763734 @default.
- W4313854540 hasRelatedWork W4288853838 @default.
- W4313854540 hasRelatedWork W4298388782 @default.
- W4313854540 hasRelatedWork W4312831135 @default.
- W4313854540 isParatext "false" @default.
- W4313854540 isRetracted "false" @default.
- W4313854540 workType "article" @default.