Abstract: An electronic device stores a plurality of vector representations for respective media content items in a vector space, where each vector represents a media content item. The electronic device receives a first set of input parameters representing a previous session of a user of the media-providing service where the previous session included two or more of the respective media content items. The electronic device then receives a second set of input parameters representing a current context of the user and provides the first set of input parameters and the second set of input parameters to a neural network to generate a prediction vector for a current session. The prediction vector is embedded in the vector space.

Abstract: An end effector for handling a sheet of flexible material. The end effector includes a support frame and manipulator assemblies, each attached to the support frame by a support mount. The manipulator assemblies include a holding tool having a lifting surface. The manipulator assemblies include a linear actuator, and the holding tool is connected to the linear actuator by a multiaxial joint. A drive provides—the displacement of the holding tool by the linear actuator. The end effector includes resilient members each rigidly affixed to two adjacent holding tools and positioned in a space providing a mutual distance between opposing faces of the adjacent holding tools, where each holding tool is connected to—adjacent holding tools—by the resilient members. The resilient members are configured to non-permanently deform in the space when adjacent holding tools are displaced relative to each other along displacement axes.

Abstract: A reinforcement learning ranker can take into account previously-recommended media content items to produce a ranked list of media content items to recommend next. The ranker finds a policy that gives the probability of sampling a media content item given a state. The policy is learned such that it maximizes a reward. A reward function associated with the media content item can be defined with respect to whether the user finds the media content item relevant (likelihood that the user will like the media content item) and a diversity score of the media content item.

Abstract: An electronic device stores a plurality of vector representations for respective media content items in a vector space, where each vector represents a media content item. The electronic device receives a first set of input parameters representing a previous session of a user of the media-providing service where the previous session included two or more of the respective media content items. The electronic device then receives a second set of input parameters representing a current context of the user and provides the first set of input parameters and the second set of input parameters to a neural network to generate a prediction vector for a current session. The prediction vector is embedded in the vector space.