Abstract: The invention relates to aqueous polymer dispersions comprising polymerized units issued from the following monomers with related weight proportions based on 100 parts of weight of a)+b): a) 50 to 90% of a monoethylenically unsaturated non-ionic monomer, b) 10 to 50% of vinyl aromatic monomer or methyl methacrylate, c) 1 to 5% of a hydroxy-containing (meth)acrylate, d) 0.1 to 3% of acid monomer selected from d1) acid monomer selected from monocarboxylic or dicarboxylic acid monomers and d2) acid monomer selected from phosphorous-based or sulfur-based strong acid monomers or/and their salts, or from the mixtures of d1) and d2), e) 0.001 to 0.5% of chain transfer agent, and f) 0 to 1% one of internal crosslinker bearing at least two copolymerisable ethylenic unsaturations, the polymer having a Tg measured by DSC from ?30 to 0° C. and a gel content of 0 to 60%, formed during polymerization.

Abstract: Systems and methods of subscriber retention analysis are disclosed. A system is configured to obtain an instance of a current subscriber data for the first current subscriber subscribed to a product for a first amount of time and configured to provide the first instance of the current subscriber data to a machine learning (ML) classification model. Training the ML classification model is based on a plurality of data sets as training data. Each data set includes an instance of historic subscriber data over the first amount of time of a subscription for a historic subscriber. The system is also configured to generate, using the ML classification model, a predicted likelihood in retaining the first current subscriber based on the first instance of the current subscriber data.

Abstract: A method may include receiving, from a software application, pre-signup data of a user, embedding the pre-signup data to obtain a pre-signup data vector, generating, by applying a topic model to the pre-signup data vector, a pre-signup topic vector, receiving action descriptions of actions of the software application, embedding the action descriptions to obtain action description vectors, generating, by applying the topic model to the action description vectors, action description topic vectors, generating scores by multiplying the pre-signup topic vector and the action description topic vectors, and recommending, to the user and using the scores, an action.

Abstract: A method is disclosed. The method includes: obtaining a help request associated with a user operating an application and a click stream of the user within the application; generating a feature data structure based on the help request and the click stream; generating, by applying the feature data structure to a machine learning model, a topic data structure including a plurality of scores corresponding to a plurality of topics; obtaining a plurality of topic distributions corresponding to a plurality of articles; identifying, by applying the topic data structure to the plurality of topic distributions, a subset of the plurality of articles for the user; and displaying, in response to the help request, a graphical user interface (GUI) including a plurality of links to the subset of the plurality of articles.

Abstract: A method may include receiving, from a software application, pre-signup data of a user, embedding the pre-signup data to obtain a pre-signup data vector, generating, by applying a topic model to the pre-signup data vector, a pre-signup topic vector, receiving action descriptions of actions of the software application, embedding the action descriptions to obtain action description vectors, generating, by applying the topic model to the action description vectors, action description topic vectors, generating scores by multiplying the pre-signup topic vector and the action description topic vectors, and recommending, to the user and using the scores, an action.

Abstract: Certain aspects of the present disclosure provide techniques for determining content quality of a set of content item based on generating a score for each content item. An example technique includes using a trained content quality model to generate the score for each content item component. In such example, the model is trained using a calculated set of features associated with text and metadata of a content item in a training data set as well as a quality label for the content item. The model is trained by associating the set of features with the quality label for each content item in the training data.

Abstract: The invention relates to aqueous polymer dispersions comprising polymerized units issued from the following monomers with related weight proportions based on 100 parts of weight of a)+b): a) 50 to 90% of a monoethylenically unsaturated non-ionic monomer, b) 10 to 50% of vinyl aromatic monomer or methyl methacrylate, c) 1 to 5% of a hydroxy-containing (meth)acrylate, d) 0.1 to 3% of acid monomer selected from d1) acid monomer selected from monocarboxylic or dicarboxylic acid monomers and d2) acid monomer selected from phosphorous-based or sulfur-based strong acid monomers or/and their salts, or from the mixtures of d1) and d2), e) 0.001 to 0.5% of chain transfer agent, and f) 0 to 1% one of internal crosslinker bearing at least two copolymerisable ethylenic unsaturations, the polymer having a Tg measured by DSC from ?30 to 0° C. and a gel content of 0 to 60%, formed during polymerization.

Abstract: One or more embodiments are directed to identifying documents with topic vectors by training a machine learning model with a training documents generated from text collections, receiving, after generating a list of topic vectors for the plurality of text collections, an additional text collection, and generating an additional topic vector for the additional text collection without training the machine learning model on the additional text collection. One or more embodiments further include updating the list of topic vectors with additional topic vectors that includes the additional topic vector, receiving a first topic vector based on a first text collection generated in response to user interaction, and matching the first topic vector to the additional topic vector. One or more embodiments further include presenting a link corresponding to the additional text collection in response to matching the first topic vector to the additional topic vector.