公开(公告)号：US20200033144A1

公开(公告)日：2020-01-30

申请号：US16047908

申请日：2018-07-27

Applicant: Adobe Inc.

Inventor： Fan Du ,  Sana Malik Lee ,  Georgios Theocharous ,  Eunyee Koh

IPC: G01C21/34 ,  G06Q10/04 ,  H04W4/021 ,  H04W4/024

Abstract: The present disclosure relates to generating and modifying recommended event sequences utilizing a dynamic user preference interface. For example, in one or more embodiments, the system generates a recommended event sequence using a recommendation model trained based on a plurality of historical event sequences. The system then provides, for display via a client device, the recommendation, a plurality of interactive elements for entry of user preferences, and a visual representation of historical event sequences. Upon detecting input of user preferences, the system can modify a reward function of the recommendation model and provide a modified recommended event sequence together with the plurality of interactive elements. In one or more embodiments, as a user enters user preferences, the system additionally modifies the visual representation to display subsets of the plurality of historical event sequences corresponding to the preferences.

公开(公告)号：US20190279096A1

公开(公告)日：2019-09-12

申请号：US15914285

申请日：2018-03-07

Applicant: Adobe Inc.

Inventor： Frits de Nijs ,  Georgios Theocharous

IPC: G06N5/02 ,  G06F17/30

Abstract: The present disclosure relates to recommending points of interest to a plurality of users based on a type of each user as well as constraints associated with the points of interest. For example, one or more embodiments determine a user type for each user and determine user preferences based on the user type. Additionally, the system can determine resource constraints associated with each point of interest, indicating limitations on the capacity of each associated resource. The system can then provide recommendations to the plurality of users based on the user types and the resource constraints. In particular, the system can recommend points of interest that satisfy the preferences corresponding to each user type subject to the resource constraints of each point of interest. For example, one or more embodiments involve solving a linear program that takes into account user types to obtain recommendation policies subject to the resource constraints.