Matches in SemOpenAlex for { <https://semopenalex.org/work/W3204808432> ?p ?o ?g. }
- W3204808432 endingPage "1" @default.
- W3204808432 startingPage "1" @default.
- W3204808432 abstract "Recently, factorization machine and its variants have shown promising results for context-aware recommender systems (CARS), especially when combined with deep neural networks. Among them, convolutional factorization machine (CFM) is a prominent example. The key to the success of CFM is its 3D convolutional architecture for capturing complex interactions on top of embedded features. However, the resultant computational cost can also be demanding. Moreover, the feature embedding scheme of CFM and other factorization models can be potentially vulnerable to noise. To tackle these issues, in this study we propose two models, namely, the fast convolutional factorization machine (FCFM) that slims down the complete pairwise feature interaction for higher computational efficiency, and adversarial fast convolutional factorization machine (AFCFM) that further enhances the robustness of the model by introducing adversarial noise to the feature interaction image generated by the model. Experimental results on four benchmark datasets prove that the proposed FCFM is nearly five times faster than CFM with competitive performance, while AFCFM improves the performance of the state-of-the-art models by about 8% with higher efficiency than CFM." @default.
- W3204808432 created "2021-10-11" @default.
- W3204808432 creator A5007481575 @default.
- W3204808432 creator A5034613739 @default.
- W3204808432 creator A5044647716 @default.
- W3204808432 creator A5054590783 @default.
- W3204808432 creator A5073501391 @default.
- W3204808432 date "2021-01-01" @default.
- W3204808432 modified "2023-10-14" @default.
- W3204808432 title "Fast Convolutional Factorization Machine with Enhanced Robustness" @default.
- W3204808432 cites W1967661515 @default.
- W3204808432 cites W2002834872 @default.
- W3204808432 cites W2069870183 @default.
- W3204808432 cites W2094286023 @default.
- W3204808432 cites W2097117768 @default.
- W3204808432 cites W2101409192 @default.
- W3204808432 cites W2112430581 @default.
- W3204808432 cites W2165949563 @default.
- W3204808432 cites W2194775991 @default.
- W3204808432 cites W2295739661 @default.
- W3204808432 cites W2306941105 @default.
- W3204808432 cites W2340502990 @default.
- W3204808432 cites W2475334473 @default.
- W3204808432 cites W2517540742 @default.
- W3204808432 cites W2539247542 @default.
- W3204808432 cites W2548570154 @default.
- W3204808432 cites W2565948352 @default.
- W3204808432 cites W2604662567 @default.
- W3204808432 cites W2605350416 @default.
- W3204808432 cites W2782298158 @default.
- W3204808432 cites W2793768763 @default.
- W3204808432 cites W2798868970 @default.
- W3204808432 cites W2798881875 @default.
- W3204808432 cites W2802187397 @default.
- W3204808432 cites W2803831897 @default.
- W3204808432 cites W2900229157 @default.
- W3204808432 cites W2912258029 @default.
- W3204808432 cites W2962712142 @default.
- W3204808432 cites W2962907114 @default.
- W3204808432 cites W2963323306 @default.
- W3204808432 cites W2963446712 @default.
- W3204808432 cites W2963655167 @default.
- W3204808432 cites W2964052347 @default.
- W3204808432 cites W3011919166 @default.
- W3204808432 cites W3045200674 @default.
- W3204808432 cites W3081170586 @default.
- W3204808432 cites W3095937012 @default.
- W3204808432 cites W3098723082 @default.
- W3204808432 cites W3100278010 @default.
- W3204808432 cites W4231054948 @default.
- W3204808432 cites W4234816175 @default.
- W3204808432 doi "https://doi.org/10.1109/tkde.2021.3116352" @default.
- W3204808432 hasPublicationYear "2021" @default.
- W3204808432 type Work @default.
- W3204808432 sameAs 3204808432 @default.
- W3204808432 citedByCount "1" @default.
- W3204808432 countsByYear W32048084322022 @default.
- W3204808432 crossrefType "journal-article" @default.
- W3204808432 hasAuthorship W3204808432A5007481575 @default.
- W3204808432 hasAuthorship W3204808432A5034613739 @default.
- W3204808432 hasAuthorship W3204808432A5044647716 @default.
- W3204808432 hasAuthorship W3204808432A5054590783 @default.
- W3204808432 hasAuthorship W3204808432A5073501391 @default.
- W3204808432 hasBestOaLocation W32048084321 @default.
- W3204808432 hasConcept C104317684 @default.
- W3204808432 hasConcept C11413529 @default.
- W3204808432 hasConcept C119857082 @default.
- W3204808432 hasConcept C13280743 @default.
- W3204808432 hasConcept C138885662 @default.
- W3204808432 hasConcept C153180895 @default.
- W3204808432 hasConcept C154945302 @default.
- W3204808432 hasConcept C185592680 @default.
- W3204808432 hasConcept C185798385 @default.
- W3204808432 hasConcept C187834632 @default.
- W3204808432 hasConcept C205649164 @default.
- W3204808432 hasConcept C2776401178 @default.
- W3204808432 hasConcept C41008148 @default.
- W3204808432 hasConcept C41608201 @default.
- W3204808432 hasConcept C41895202 @default.
- W3204808432 hasConcept C55493867 @default.
- W3204808432 hasConcept C63479239 @default.
- W3204808432 hasConcept C81363708 @default.
- W3204808432 hasConceptScore W3204808432C104317684 @default.
- W3204808432 hasConceptScore W3204808432C11413529 @default.
- W3204808432 hasConceptScore W3204808432C119857082 @default.
- W3204808432 hasConceptScore W3204808432C13280743 @default.
- W3204808432 hasConceptScore W3204808432C138885662 @default.
- W3204808432 hasConceptScore W3204808432C153180895 @default.
- W3204808432 hasConceptScore W3204808432C154945302 @default.
- W3204808432 hasConceptScore W3204808432C185592680 @default.
- W3204808432 hasConceptScore W3204808432C185798385 @default.
- W3204808432 hasConceptScore W3204808432C187834632 @default.
- W3204808432 hasConceptScore W3204808432C205649164 @default.
- W3204808432 hasConceptScore W3204808432C2776401178 @default.
- W3204808432 hasConceptScore W3204808432C41008148 @default.
- W3204808432 hasConceptScore W3204808432C41608201 @default.
- W3204808432 hasConceptScore W3204808432C41895202 @default.
- W3204808432 hasConceptScore W3204808432C55493867 @default.