Methods and systems for providing digital assistance. One system includes at least one electronic processor configured to access data representing historical tasks performed by a user through at least one user device, determine, based on the data, a first plurality of tasks associated with a life event of the user, and store an association between the first plurality of tasks and the life event. The electronic processor is also configured to, in response to a current occurrence of the life event experienced by the user, retrieve the association and generate a user interface for display to the user, the user interface including a second plurality of tasks for addressing the current occurrence of the life event based on the first plurality of tasks.

Personalized adaptive task framework for user life events

Cognitively identifying a propensity for obtaining prospective entities

The invention discloses an intelligent financing recommendation method based on user continuous behavior sequence characteristics. The method comprises the steps of obtaining financial product attribute data and user attribute data, performing data cleaning and standardized management, classifying the data, performing transformer model architecture training on the data, inputting information intoa trained model, outputting personal financial recommendation by the model, recommending the personal financial recommendation to a user through multiple channels, and obtaining feedback information.In the present invention, according to the intelligent financial management recommendation method, the feature extraction is carried out on user information data uploaded by a user terminal. Particularly, a sequence signal of a user behavior sequence is captured by using a Transformer new architecture model. The previous behavior sequence characteristics of the user are combined, so that the accuracy of recommending financial products to the user is improved. Therefore, the click rate and the purchase rate of the financial product by the user are automatically improved. The intelligent financial recommendation system can be safely used. The knowledge degree between the user and the bank financial product is enhanced. The mutual benefit and mutual benefit effects are achieved for the user and the bank.

Intelligent financing recommendation method based on user continuous behavior sequence characteristics

Methods and apparatuses are described for automated predictive product recommendations using reinforcement learning. A server captures historical activity data associated with a plurality of users. The server generates a context vector for each user, the context vector comprising a multidimensional array corresponding to historical activity data. The server transforms each context vector into a context embedding. The server assigns each context embedding to an embedding cluster. The server determines, for each context embedding, (i) an overall likelihood of successful attempt and (ii) an incremental likelihood of success associated products available for recommendation. The server calculates, for each context embedding, an incremental income value associated with each of the likelihoods of success. The server aggregates (i) the overall likelihood of successful attempt, (ii) the likelihoods of success, and (iii) the incremental income values into a recommendation matrix. The server generates instructions to recommend products based upon the recommendation matrix.

Automated predictive product recommendations using reinforcement learning

The invention discloses a lottery user activity prediction method, which comprises the following steps: acquiring original user data; extracting and converting the original user data; classifying andloading the original user data into a database in a specified format; and loading the original user data into a database in a specified format. Preprocessing the original user data stored in the database to obtain multi-dimensional user data; Obtaining a prediction feature set related to user activity according to the multi-dimensional user data; inputting The prediction feature set into a pre-trained GBDT algorithm-based activity prediction model to predict user activity. Correspondingly, the invention also discloses a lottery user activity prediction system, a terminal device and a computer-readable storage medium. The technical proposal of the invention can reduce the prediction difficulty of the lottery user activity and improve the prediction accuracy.

Lottery user activity prediction method, system, terminal device, and storage medium

Certain embodiments involve learning user preferences and predicting user behavior based on sequential user behavior data. For example, a system obtains data about a sequence of prior actions taken by multiple users. The system determines a similarity between a prior action taken by the various users and groups the various users into groups or clusters based at least in part on the similarity. The system trains a machine-learning algorithm such that the machine-learning algorithm can be used to predict a subsequent action of a user among the various users based on the various clusters. The system further obtains data about a current action of a new user and determines which of the clusters to associate with the new user based on the new user's current action. The system determines an action to be recommended to the new user based on the cluster associated with the new user. The action can include a series or sequence of actions to be taken by the new user. The system further provides the series or sequence of actions or an action of the series or sequence to the new user.

Mandana Hamidi Haines

Papers

Learning user preferences using sequential user behavior data to predict user behavior and provide recommendations