Patent application EP19168125A (EP), filed 2019-04-09
Publication number: EP3561735A1
Title: INTEGRATING DEEP LEARNING INTO GENERALIZED ADDITIVE MIXED-EFFECT (GAME) FRAMEWORKS
Abstract: In an example embodiment, knowledge discovery using deep learning is combined with the scalability and personalization capabilities of generalized additive mixed effect (GAME) modeling. Specifically, features learned in a last fully connected layer of a deep learning model may be used to augment features used in a fixed or random effects training portion of a GAME model.
IPC codes: G06N  20/    20            A I
G06N   3/    04            A I
G06N   3/    08            A I
G06N   5/    00            A I
G06Q  10/    06            A I
G06Q  10/    10            A I
Claims: number: 1
text: A system comprising:
a computer-readable medium having instructions stored thereon, which, when executed by a processor, cause the system to:
number: 2
text: The system of claim 1, wherein the instructions further cause the system to:
number: 3
text: The system of claim 1, wherein the instructions further cause the system to:
number: 4
text: The system of claim 3 wherein the instructions further cause the system to:
during training of the fixed effect training portion and the random effects training portion, augment either the third set of features or fourth set of features with the sixth set of features from the second deep learning model, causing the GAME model to be trained based on the second set of features learned by the second deep learning model.
number: 5
text: A computerized method comprising:
number: 6
text: The computerized method of claim 5, further comprising:
number: 7
text: The computerized method of claim 5, further comprising:
number: 8
text: The computerized method of claim 7, further comprising:
during training of the fixed effect training portion and the random effects training portion, augmenting either the third set of features or fourth set of features with the sixth set of features from the second deep learning model, causing the GAME model to be trained based on the second set of features learned by the second deep learning model.
number: 9
text: The system of claim 1 or computerized method of claim 5, wherein the first prediction is predicting the same type of output as the third prediction.
number: 10
text: A non-transitory machine-readable storage medium comprising instructions which, when implemented by one or more machines, cause the one or more machines to perform operations comprising:
obtaining a first set of features derived from data in a social networking service;
number: 11
text: The non-transitory machine-readable storage medium of claim 10, wherein the operations further comprise:
number: 12
text: The non-transitory machine-readable storage medium of claim 10, wherein the operations further comprise:
number: 13
text: The non-transitory machine-readable storage medium of claim 12, wherein the operations further comprise:
during training of the fixed effect training portion and the random effects training portion, augmenting either the third set of features or fourth set of features with the sixth set of features from the second deep learning model, causing the GAME model to be trained based on the second set of features learned by the second deep learning model.
number: 14
text: The system of claim 1, computerized method of claim 5, or non-transitory machine-readable storage medium of claim 10, wherein the first set of features and the second set of features are identical.
number: 15
text: The system of claim 1, computerized method of claim 5, or non-transitory machine-readable storage medium of claim 10, wherein the first prediction is predicting the same type of output as the second prediction.
Office action: search_report, dated 2019-10-30
Prior art cited: ref_id: US2017323268A1
ref_type: patent
severity: background
ref_id: - LIANG TANG ET AL: "An Empirical Study on Recommendation with Multiple Types of Feedback", PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD '16, ACM PRESS, NEW YORK, NEW YORK, USA, 13 August 2016 (2016-08-13), pages 283 - 292, XP058276819, ISBN: 978-1-4503-4232-2, DOI: 10.1145/2939672.2939690
ref_type: npl
severity: background
ref_id: - HENG-TZE CHENG ET AL: "Wide & Deep Learning for Recommender Systems", DEEP LEARNING FOR RECOMMENDER SYSTEMS, ACM, 2 PENN PLAZA, SUITE 701 NEW YORK NY 10121-0701 USA, 15 September 2016 (2016-09-15), pages 7 - 10, XP058280488, ISBN: 978-1-4503-4795-2, DOI: 10.1145/2988450.2988454
ref_type: npl
severity: background
ref_id: US2017323268
ref_type: patent
severity: background
ref_id: - LIANG TANG ET AL, "An Empirical Study on Recommendation with Multiple Types of Feedback", PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD '16, ACM PRESS, NEW YORK, NEW YORK, USA, (20160813), doi:10.1145/2939672.2939690, ISBN 978-1-4503-4232-2, pages 283 - 292, XP058276819 [A] 1-15 * abstract * * Section 1, 3 *
ref_type: npl
severity: background
ref_id: - HENG-TZE CHENG ET AL, "Wide & Deep Learning for Recommender Systems", DEEP LEARNING FOR RECOMMENDER SYSTEMS, ACM, 2 PENN PLAZA, SUITE 701 NEW YORK NY 10121-0701 USA, (20160915), doi:10.1145/2988450.2988454, ISBN 978-1-4503-4795-2, pages 7 - 10, XP058280488 [A] 1-15 * abstract * * Sections 3.3, 4.2 *
ref_type: npl
severity: background
Final disposition: pending (2019-10-30)