FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3088138397> ?p ?o ?g. }

• W3088138397 endingPage "95" @default.
• W3088138397 startingPage "80" @default.
• W3088138397 abstract "In predictive modeling, firms often deal with high-dimensional data that span multiple channels, websites, demographics, purchase types, and product categories. Traditional customer response models rely heavily on feature engineering, and their performance depends on the analyst's domain knowledge and expertise to craft relevant predictors. As the complexity of data increases, however, traditional models grow exponentially complicated. In this paper, we demonstrate that long-short term memory (LSTM) neural networks, which rely exclusively on raw data as input, can predict customer behaviors with great accuracy. In our first application, a model outperforms standard benchmarks. In a second, more realistic application, an LSTM model competes against 271 hand-crafted models that use a wide variety of features and modeling approaches. It beats 269 of them, most by a wide margin. LSTM neural networks are excellent candidates for modeling customer behavior using panel data in complex environments (e.g., direct marketing, brand choices, clickstream data, churn prediction)." @default.
• W3088138397 created "2020-10-01" @default.
• W3088138397 creator A5017837767 @default.
• W3088138397 creator A5091502236 @default.
• W3088138397 date "2021-02-01" @default.
• W3088138397 modified "2023-09-29" @default.
• W3088138397 title "LSTM Response Models for Direct Marketing Analytics: Replacing Feature Engineering with Deep Learning" @default.
• W3088138397 cites W1498436455 @default.
• W3088138397 cites W1689711448 @default.
• W3088138397 cites W1996074959 @default.
• W3088138397 cites W2001971142 @default.
• W3088138397 cites W2015122554 @default.
• W3088138397 cites W2019644186 @default.
• W3088138397 cites W2025348367 @default.
• W3088138397 cites W2026219386 @default.
• W3088138397 cites W2026710079 @default.
• W3088138397 cites W2029452845 @default.
• W3088138397 cites W2036207307 @default.
• W3088138397 cites W2037963950 @default.
• W3088138397 cites W2052065931 @default.
• W3088138397 cites W2061670499 @default.
• W3088138397 cites W2064675550 @default.
• W3088138397 cites W2083833236 @default.
• W3088138397 cites W2100688874 @default.
• W3088138397 cites W2107878631 @default.
• W3088138397 cites W2110562768 @default.
• W3088138397 cites W2111837988 @default.
• W3088138397 cites W2122825543 @default.
• W3088138397 cites W2141085630 @default.
• W3088138397 cites W2151987718 @default.
• W3088138397 cites W2153053867 @default.
• W3088138397 cites W2158101271 @default.
• W3088138397 cites W2192203593 @default.
• W3088138397 cites W2334266040 @default.
• W3088138397 cites W2803015509 @default.
• W3088138397 cites W2911964244 @default.
• W3088138397 cites W2945976633 @default.
• W3088138397 cites W3037880470 @default.
• W3088138397 cites W3121972256 @default.
• W3088138397 cites W3123614577 @default.
• W3088138397 cites W3124035032 @default.
• W3088138397 cites W3125877494 @default.
• W3088138397 doi "https://doi.org/10.1016/j.intmar.2020.07.002" @default.
• W3088138397 hasPublicationYear "2021" @default.
• W3088138397 type Work @default.
• W3088138397 sameAs 3088138397 @default.
• W3088138397 citedByCount "33" @default.
• W3088138397 countsByYear W30881383972020 @default.
• W3088138397 countsByYear W30881383972021 @default.
• W3088138397 countsByYear W30881383972022 @default.
• W3088138397 countsByYear W30881383972023 @default.
• W3088138397 crossrefType "journal-article" @default.
• W3088138397 hasAuthorship W3088138397A5017837767 @default.
• W3088138397 hasAuthorship W3088138397A5091502236 @default.
• W3088138397 hasBestOaLocation W30881383971 @default.
• W3088138397 hasConcept C108583219 @default.
• W3088138397 hasConcept C110875604 @default.
• W3088138397 hasConcept C119857082 @default.
• W3088138397 hasConcept C127613066 @default.
• W3088138397 hasConcept C130436687 @default.
• W3088138397 hasConcept C134306372 @default.
• W3088138397 hasConcept C136197465 @default.
• W3088138397 hasConcept C136764020 @default.
• W3088138397 hasConcept C138744977 @default.
• W3088138397 hasConcept C138885662 @default.
• W3088138397 hasConcept C154945302 @default.
• W3088138397 hasConcept C207685749 @default.
• W3088138397 hasConcept C2522767166 @default.
• W3088138397 hasConcept C2776401178 @default.
• W3088138397 hasConcept C2778827112 @default.
• W3088138397 hasConcept C33923547 @default.
• W3088138397 hasConcept C36503486 @default.
• W3088138397 hasConcept C41008148 @default.
• W3088138397 hasConcept C41895202 @default.
• W3088138397 hasConcept C50644808 @default.
• W3088138397 hasConcept C512338625 @default.
• W3088138397 hasConcept C774472 @default.
• W3088138397 hasConcept C79158427 @default.
• W3088138397 hasConcept C83209312 @default.
• W3088138397 hasConceptScore W3088138397C108583219 @default.
• W3088138397 hasConceptScore W3088138397C110875604 @default.
• W3088138397 hasConceptScore W3088138397C119857082 @default.
• W3088138397 hasConceptScore W3088138397C127613066 @default.
• W3088138397 hasConceptScore W3088138397C130436687 @default.
• W3088138397 hasConceptScore W3088138397C134306372 @default.
• W3088138397 hasConceptScore W3088138397C136197465 @default.
• W3088138397 hasConceptScore W3088138397C136764020 @default.
• W3088138397 hasConceptScore W3088138397C138744977 @default.
• W3088138397 hasConceptScore W3088138397C138885662 @default.
• W3088138397 hasConceptScore W3088138397C154945302 @default.
• W3088138397 hasConceptScore W3088138397C207685749 @default.
• W3088138397 hasConceptScore W3088138397C2522767166 @default.
• W3088138397 hasConceptScore W3088138397C2776401178 @default.
• W3088138397 hasConceptScore W3088138397C2778827112 @default.
• W3088138397 hasConceptScore W3088138397C33923547 @default.
• W3088138397 hasConceptScore W3088138397C36503486 @default.
• W3088138397 hasConceptScore W3088138397C41008148 @default.
• W3088138397 hasConceptScore W3088138397C41895202 @default.

• next