Patent application EP17151624A (EP), filed 2017-01-16
Publication number: EP3239917A1
Title: RECOMMENDATION GENERATION
Abstract: A method may include generating a personalized rating vector associated with a querying user. The personalized rating vector may relate personalized ratings to options. The personalized rating vector may be generated via element-wise multiplication of a user vector, a relevance weight, and a characteristics vector. The user vector may relate user ratings to the querying user and to the options. The relevance weight may be associated with a characteristic of the querying user. The characteristics vector may relate characteristics ratings to a user characteristic and to the options. The user characteristic may correspond at least in part to the characteristic of the querying user. The method may further include generating a recommendation for the querying user based at least in part on the personalized rating vector, the recommendation associated with at least one option of the options of the personalized rating vector
IPC codes: G06N  20/    00            A I
G06Q  30/    06            A I
Claims: number: 1
text: A method comprising:
number: 2
text: The method of claim 1, wherein the user vector corresponds to a portion of a ratings matrix associated with the querying user, the ratings matrix relating users ratings to the options of the personalized rating matrix, the users associated with the users ratings including the querying user.
number: 3
text: The method of claim 2, wherein the personalized rating vector is equal to the following expression: X E U , ∗ ∑ k α pk − μs ∗ X C C ,
number: 11
text: The method of any of claims 8 to 10, wherein:
number: 12
text: The method of claim 10, further comprising:
number: 13
text: The method of claim 12, wherein the constructing the partial relational matrices, the collectively factorizing the partial relational matrices, and the generating the complete relational matrices are performed based at least in part on collective matrix factorization with group-sparse embeddings.
number: 14
text: A non-transitory computer-readable medium having encoded therein programing code executable by a processor to perform operations comprising:
Office action: search_report, dated 2017-11-01
Prior art cited: ref_id: - KLAMI ET AL.: "Group-sparse Embeddings in Collective Matrix Factorization", INTERNATIONAL CONFERENCE ON REPRESENTATIONAL LEARNING, 2014
ref_type: npl
severity: background
ref_id: WO2012155329A1
ref_type: patent
severity: background
ref_id: - ANDREW  S. TANENBAUM ET AL: "Distributed Systems: Principles and Paradigms (2nd Edition)", 12 October 2006, PRENTICE HALL, ISBN: 978-0-13-239227-3, pages: 0 - 68, XP055164378
ref_type: npl
severity: background
ref_id: - KIM FALK: "Practical Recommender Systems", vol. MEAP v1, 24 July 2015, MANNING PUBLICATIONS, ISBN: 978-1-61729-270-5, pages: ToC,Ch01, XP055345122
ref_type: npl
severity: background
ref_id: - WIKIPEDIA: "Mobile device", INTERNET ARTICLE, 14 April 2016 (2016-04-14), XP055345131, Retrieved from the Internet <URL:https://en.wikipedia.org/w/index.php?title=Mobile_device&oldid=715247550> [retrieved on 20170213]
ref_type: npl
severity: background
ref_id: WO2015188349A1
ref_type: patent
severity: background
ref_id: US2014156231A1
ref_type: patent
severity: background
ref_id: - ANDREW  S. TANENBAUM ET AL: "Distributed Systems: Principles and Paradigms (2nd Edition)", 12 October 2006, PRENTICE HALL, ISBN: 978-0-13-239227-3, pages: ToC,Ch01 - Ch03,Ch10,Ch12, XP055272991
ref_type: npl
severity: background
ref_id: - WIKIPEDIA: "Mobile Web", INTERNET ARTICLE, 25 April 2016 (2016-04-25), XP055529328, Retrieved from the Internet <URL:https://en.wikipedia.org/w/index.php?title=Mobile_Web&oldid=717024768> [retrieved on 20181203]
ref_type: npl
severity: background
ref_id: - WIKIPEDIA: "Web application", INTERNET ARTICLE, 27 April 2016 (2016-04-27), XP055529331, Retrieved from the Internet <URL:https://en.wikipedia.org/w/index.php?title=Web_application&oldid=717355790> [retrieved on 20181203]
ref_type: npl
severity: background
ref_id: - ARTO KLAMI ET AL: "Group-sparse Embeddings in Collective Matrix Factorization", INTERNATIONAL CONFERENCE ON LEARNING REPRESENTATIONS 2014, 18 February 2014 (2014-02-18), XP055529388, Retrieved from the Internet <URL:https://arxiv.org/abs/1312.5921> [retrieved on 20181203]
ref_type: npl
severity: background
ref_id: WO2012155329
ref_type: patent
severity: background
ref_id: - Andrew S. Tanenbaum ET AL, Distributed Systems: Principles and Paradigms (2nd Edition), Prentice Hall, (20061012), pages 0 - 68, ISBN 978-0-13-239227-3, XP055164378 [I] 1-14 * figures 1-10, 1-11, 2-4, 2-5, 2-6 *
ref_type: npl
severity: background
ref_id: - Kim Falk, Practical Recommender Systems, Manning Publications, (20150724), vol. MEAP v1, page ToC,Ch01, ISBN 978-1-61729-270-5, XP055345122 [I] 1-14 * figure 1.18 *
ref_type: npl
severity: background
ref_id: - Wikipedia, "Mobile device", INTERNET ARTICLE, (20160414), URL: https://en.wikipedia.org/w/index.php?title=Mobile_device&oldid=715247550, (20170213), XP055345131 [I] 1-14 * the whole document *
ref_type: npl
severity: background
Final disposition: pending (2017-11-01)