Abstract: Disclosed are mechanisms for selecting a recommended item for a current item being viewed by a user account based on a view history of the user account with reduced bias. For a current item being viewed by the user account represented by a current node of a co-watch graph, embodiments can select a recommended item represented by an associated node in the co-watch graph likely being viewed by the user account, and determine a probability of the recommended item likely being viewed. The co-watch graph can be generated based on a view history of the user account. An edge between a first node and a second node of the co-watch graph can have a weight representing a number of co-occurrence times when the first item represented by the first node and the second item represented by the second node are viewed in sequence within a predetermined time interval.

Abstract: Disclosed herein are various embodiments, for a content display and clustering system. An example embodiment operates by receiving a request to display the plurality of content items. At each of multiple levels different pairs of content items are identified and a similarity score is computed for each pair. A subset of pairs for which their similarity score exceeds a similarity threshold for the respective level are identified and clustered. This process is repeated for one or more iterations at the same level, and then the process is repeated for each of the multiple levels. A final clustered subset is identified, and output for display, responsive to the request to display the plurality of content items.

Abstract: Disclosed are system, method and/or computer program product embodiments for improving the performance of a machine learning based algorithm used to provide a user experience to a user via a media device. An embodiment selects a first set of hyperparameter values, implements a first iteration of the algorithm based on the first set of hyperparameter values, utilizes the first iteration of the algorithm to provide a first user experience to the user, determines a response of the user to the first user experience, selects, by a hyperparameter tuning ML model implemented as a contextual multi-arm bandit model or a reinforcement learning model and based on at least the response of the user, a second set of hyperparameter values, implements a second iteration of the algorithm based on the second set of hyperparameter values, and utilizes the second iteration of the algorithm to provide a second user experience to the user.

Abstract: Disclosed are mechanisms for selecting a recommended item for a current item being viewed by a user account based on a view history of the user account with reduced bias. For a current item being viewed by the user account represented by a current node of a co-watch graph, embodiments can select a recommended item represented by an associated node in the co-watch graph likely being viewed by the user account, and determine a probability of the recommended item likely being viewed. The co-watch graph can be generated based on a view history of the user account. An edge between a first node and a second node of the co-watch graph can have a weight representing a number of co-occurrence times when the first item represented by the first node and the second item represented by the second node are viewed in sequence within a predetermined time interval.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations thereof, for training a conversational recommendation system. An embodiment generates a probabilistic pseudo-user neural network model based on at least one interest probability distribution corresponding to a pseudo-user profile. The embodiment trains, using the pseudo-user neural network model, the conversational recommendation system to learn a recommendation policy, where the conversational recommendation system includes an interest-exploration engine and a prompt-decision engine. The training includes performing an iterative learning process that includes selecting an interest-exploration strategy based on one or more of the following: an interest-exploration policy, an earlier pseudo-user response generated by the pseudo-user neural network model, content data, and pseudo-user interaction history.

Abstract: The present disclosure discloses a simple reflex intelligent agent for crawling literature data and a method for crawling literature data. The simple reflex intelligent agent includes a performance module, an environment module, a sensing module and an actuator module; the performance module is used to construct a performance objective function; the environment module constructs an environment collection for the simple reflex intelligent agent; the sensing module monitors whether system time and a number of journals have been changed; the actuator module sets targets based on the performance objective function and automatically crawls literature data.

Abstract: A gas-phase aldehyde hydrogenation catalyst, a preparation method thereof and an application thereof, where the preparation method includes: mixing an aluminum salt, a first zinc salt, silica sol, a first precipitant and water, and performing a first aging treatment to obtain a first mixed solution; mixing a copper salt, a second zinc salt, a zinc powder and water, and performing an ultrasonic treatment to obtain a second mixed solution; mixing the first mixed solution, the second mixed solution and a second precipitant, and performing a second aging treatment to obtain a third mixed solution; adding boric acid into the third mixed solution, and performing a third aging treatment to obtain a fourth mixed solution; filtering the fourth mixed solution, and sequentially drying and roasting the obtained solid product to obtain a catalyst precursor; mixing and molding the catalyst precursor with a graphite to obtain a gas-phase aldehyde hydrogenation catalyst.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for optimizing user experience/engagement and revenue. An example embodiment operates by a computer-implemented method for providing one or more advertisements to a media device. The method includes receiving, by at least one computer processor, a user state associated with a user of the media device, where the user state corresponds to a time step. The method further includes receiving a revenue value associated with the user of the media device, where the revenue value corresponds to the time step. The method also include determining an action associated with the user based on the user state and the revenue value. The action includes one or more parameters associated with the one or more advertisements. The method further includes providing the action to the user.

Abstract: A set of content items can be accessed by a community of users having a set of interests. A set of interest based clusters for the set of content items correspond to the set of interests. For a user, a recommendation system can determine a group of user interest clusters selected from the set of interest based clusters. A popularity score for each content item of the set of content items with respect to the community of users can be generated, and an interest based popularity score for a content item within the interest based cluster can be generated based on a rank of the content item based on the popularity score of the content item. Recommendation candidates for the user can be generated based on the interest based popularity score of the content item for each content item in the group of user interest clusters.

Abstract: Disclosed herein are various embodiments, for a content display and clustering system. An example embodiment operates by receiving a request to display the plurality of content items. At each of multiple levels different pairs of content items are identified and a similarity score is computed for each pair. A subset of pairs for which their similarity score exceeds a similarity threshold for the respective level are identified and clustered. This process is repeated for one or more iterations at the same level, and then the process is repeated for each of the multiple levels. A final clustered subset is identified, and output for display, responsive to the request to display the plurality of content items.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for online automatic hyperparameter tuning of a machine learning model that provides a user experience to media devices such that the machine learning model maximizes (or minimizes) an objective function. An example embodiment operates by generating an initial set of hyperparameter configurations for a machine learning model based on sampling data received from media devices over a network. The embodiment then determines, using an hyperparameter tuning method, a hyperparameter configuration based on the initial set of hyperparameter configurations that causes a training of the machine learning model using a learning algorithm to maximize an objective function. The embodiment then trains the machine learning model according to the determined hyperparameter configuration using the learning algorithm.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for candidate ranking for content recommendation. An embodiment operates by receiving category candidates over a network, wherein each of the category candidates comprises content candidates associated with one or more applications operating on media devices. The embodiment then ranks the category candidates based on a machine model trained using a learning algorithm based on the time series data, and ranks the content candidates in the each of category candidates based on the time series data. The embodiment then causes the ranked category candidates and the ranked content candidates to be outputted for display.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for stochastic candidate selection for content recommendation. An example embodiment operates by a computer-implemented method for stochastic candidate selection for content recommendation. The method includes receiving, by at least one computer processor, a first plurality of content candidates and selecting a second plurality of content candidates from the first plurality of content candidates. The method further include ranking the second plurality of content candidates based on one or more parameters and selecting a third plurality of content candidates from the ranked second plurality of content candidates. The method can further include displaying the third plurality of content candidates using a display device.

Abstract: Disclosed herein are various embodiments, for a content display and clustering system. An example embodiment operates by receiving a request to display the plurality of content items. At each of multiple levels different pairs of content items are identified and a similarity score is computed for each pair. A subset of pairs for which their similarity score exceeds a similarity threshold for the respective level are identified and clustered. This process is repeated for one or more iterations at the same level, and then the process is repeated for each of the multiple levels. A final clustered subset is identified, and output for display, responsive to the request to display the plurality of content items.

Abstract: Disclosed herein are various embodiments, for a content display and clustering system. An example embodiment operates by receiving a request to display the plurality of content items. At each of multiple levels different pairs of content items are identified and a similarity score is computed for each pair. A subset of pairs for which their similarity score exceeds a similarity threshold for the respective level are identified and clustered. This process is repeated for one or more iterations at the same level, and then the process is repeated for each of the multiple levels. A final clustered subset is identified, and output for display, responsive to the request to display the plurality of content items.

Abstract: Disclosed are mechanisms for selecting a recommended item for a current item being viewed by a user account based on a view history of the user account with reduced bias. For a current item being viewed by the user account represented by a current node of a co-watch graph, embodiments can select a recommended item represented by an associated node in the co-watch graph likely being viewed by the user account, and determine a probability of the recommended item likely being viewed. The co-watch graph can be generated based on a view history of the user account. An edge between a first node and a second node of the co-watch graph can have a weight representing a number of co-occurrence times when the first item represented by the first node and the second item represented by the second node are viewed in sequence within a predetermined time interval.

Abstract: Example parameter optimization methods and apparatus are described. In one example parameter optimization method, a data processing device obtains a Kronecker factor matrix that is used to indicate a higher-order information matrix of a neural network model, and segments the Kronecker factor matrix to obtain a plurality of square matrices. The obtained plurality of square matrices are submatrices of the Kronecker factor matrix, and main diagonal lines of the plurality of square matrices each are in a one-to-one correspondence with a part of a main diagonal line of the Kronecker factor matrix. Then, the data processing device adjusts a parameter of the neural network model based on the obtained plurality of square matrices.

Abstract: The present disclosure relates to parameter updating methods. In one example method, a parameter in a neural network model is updated for a plurality of times through a plurality of iterations. The plurality of iterations include a first iteration period and a second iteration period. In the first iteration period, an inverse matrix of an additional matrix of the neural network model is updated once based on a quantity of iterations indicated by a first update stride. In the second iteration period, the inverse matrix of the additional matrix of the neural network model is updated once based on a quantity of iterations indicated by a second update stride, where the first iteration of the second iteration period is after the last iteration of the first iteration period in an iteration sequence, and the second update stride is greater than the first update stride.

Abstract: A method and apparatus cast a material with nano-micro duplex grain structure. The apparatus includes module system, heating system casting mold and gating system, multiaxial compound motion system accompanied by the following technological characteristics; alloy smelting; after heat preservation, alloy melt is poured into the casting mold which is put into the centrifugal barrel of the six-axis motion system; then the casting mold carries out composite motion and the alloy melt starts solidification; as a result, casting Al—Si alloy block with multi-scale nano-structure includes nano-micro duplex grain group is prepared.

Abstract: A method and apparatus casts a material with nano-micro duplex grain structure. The apparatus is comprised of module system, heating system, casting mold and gating system, multiaxial compound motion system accompanied by the following technological characteristics; alloy smelting; after heat preservation, alloy melt are poured into the casting mold which is put into the centrifugal barrel of the six-axis motion system; then the casting mold carries out composite motion and the alloy melt starts solidification; as a result, casting Al—Si alloy block with multi-scale nano-structure comprising of nano-micro duplex grain group are prepared. The advantages of the present invention includes that multi-scale casting nano-structure with nano-micro duplex grain group are obtained because of the composite shear flow field generated in the alloy melt.