Abstract: A system may automatically generate a predictive machine learning model by automatically performing various processes based on an analysis of the data as well as metadata associated with the data. The system may accept a selection of data and a prediction field from the data. The system may automatically generate a set of features based on the data and may automatically remove certain features that cause inaccuracies in the model. The system may balance the data based on a representation rate of certain outcomes. The system may train and select a model based on several candidate models. The system may then perform the predictions based on the selected model and send an indication of the predictions to a user.

Abstract: A system may automatically generate a predictive machine learning model by automatically performing various processes based on an analysis of the data as well as metadata associated with the data. The system may accept a selection of data and a prediction field from the data. The system may automatically generate a set of features based on the data and may automatically remove certain features that cause inaccuracies in the model. The system may balance the data based on a representation rate of certain outcomes. The system may train and select a model based on several candidate models. The system may then perform the predictions based on the selected model and send an indication of the predictions to a user.

Abstract: Methods and systems are provided to determine suitable hyperparameters for a machine learning model and/or feature engineering process. A suitable machine learning model and associated hyperparameters are determined by analyzing a dataset. Suitable hyperparameter values for compatible machine learning models having one or more hyperparameters in common and a compatible dataset schema are identified. Hyperparameters may be ranked according to each of their respective influences on a model performance metrics, and hyperparameter values identified as having greater influence may be more aggressively searched.

Abstract: A method for improving mass messaging in an electronic messaging system includes receiving recipient data describing a response of each of one or more recipients to receiving a prior message, generating predictor data based on the recipient data, where the predictor data indicates a plurality of predictors of recipient behavior in response to a message, identifying one or more top predictors of recipient behavior, the one or more top predictors being selected from among the plurality of predictors based on preferred recipient behaviors, generating, for each of the one or more recipients and from the recipient data, one or more predictive scores for each combination of top predictor and recipient, and assigning, based on one or more predictive scores of a specific recipient, the specific recipient to a specific persona, wherein the specific persona describes an expected behavior of the recipient.

Abstract: Methods, systems, and devices for multi-tenant workflow processing are described. In some cases, a cloud platform may utilize a set of pre-defined batch processes (e.g., workflow templates) and tenant-specific configurations for instantiating and executing tenant-specific batch processes for each tenant of a user. As such, the cloud platform may utilize common data process workflows for each tenant, where a configuration specifies tenant-specific information for the common data process workflows. The workflow templates may include a set of job definitions (e.g., actions for a server to execute) and a schedule defining the frequency for running the templates for a specific project. The configurations may indicate a tenant to execute the workflow templates for, and may include tenant-specific information to override default template information.

Abstract: Methods and systems are provided to determine suitable hyperparameters for a machine learning model and/or feature engineering process. A suitable machine learning model and associated hyperparameters are determined by analyzing a dataset. Suitable hyperparameter values for compatible machine learning models having one or more hyperparameters in common and a compatible dataset schema are identified. Hyperparameters may be ranked according to each of their respective influences on a model performance metrics, and hyperparameter values identified as having greater influence may be more aggressively searched.

Abstract: Techniques for optimizing a guided edit process for editing a member profile page are described. According to various embodiments, profile edit task information associated with a member of an online social network service is accessed, the profile edit task information identifying one or more candidate profile edit tasks to be performed to update a member profile page of the member. Thereafter, if it is determined that the member recently completed a difficult profile edit task, a difficult candidate profile edit task is identified, and the member is prompted to perform the difficult candidate profile edit task. If it is determined that the member has not recently completed a difficult profile edit task, an easy candidate profile edit task is identified, and the member is prompted to perform the easy candidate profile edit task.

Abstract: Methods, systems, and devices for multi-tenant workflow processing are described. In some cases, a cloud platform may utilize a set of pre-defined batch processes (e.g., workflow templates) and tenant-specific configurations for instantiating and executing tenant-specific batch processes for each tenant of a user. As such, the cloud platform may utilize common data process workflows for each tenant, where a configuration specifies tenant-specific information for the common data process workflows. The workflow templates may include a set of job definitions (e.g., actions for a server to execute) and a schedule defining the frequency for running the templates for a specific project. The configurations may indicate a tenant to execute the workflow templates for, and may include tenant-specific information to override default template information.

Abstract: A system may automatically generate a predictive machine learning model by automatically performing various processes based on an analysis of the data as well as metadata associated with the data. The system may accept a selection of data and a prediction field from the data. The system may automatically generate a set of features based on the data and may automatically remove certain features that cause inaccuracies in the model. The system may balance the data based on a representation rate of certain outcomes. The system may train and select a model based on several candidate models. The system may then perform the predictions based on the selected model and send an indication of the predictions to a user.

Abstract: A method and system to suggest keywords to a social network member is described. A suggested keywords system, in one example embodiment, examines phrases that appear in profiles maintained by the on-line social networking system that are similar to the target profile and identifies those words and phrases that are most prominent in these profiles, utilizing a graph-based approach. These most prominent words and phrases may be presented to the target member as suggested keywords to be included in the member's professional summary.

Abstract: A suggested keywords system is configured for identifying phrases, which are most relevant to experience and expertise of a professional network member, and which the member may be interested in weaving into their profile summary. The suggested keywords system generates a model, for each phrase, that calculates probability of that phrase being present in a profile that is characterized by the absence of certain attributes and by the presence of certain attributes. Based on the model, the suggested keywords system calculates a ranking value for the phrase for a particular target profile. The phrases with the higher rank are considered to be more relevant in describing professional background of the target member. A certain number of phrases that have the highest ranking are presented to the member as suggested keywords to be included in their professional summary.

Abstract: In accordance with embodiments, there are provided mechanisms and methods for facilitating single model-based behavior predictions in an on-demand services environment in an on-demand services environment according to one embodiment. In one embodiment and by way of example, a method comprises collecting, by a model selection and application server device (“model device”), information associated with customers of a tenant, and extracting, from the information, behavior traits of the customers as they relate to products or services offered by the tenant.

Abstract: A method for improving mass messaging in an electronic messaging system includes receiving recipient data describing a response of each of one or more recipients to receiving a prior message, generating predictor data based on the recipient data, where the predictor data indicates a plurality of predictors of recipient behavior in response to a message, identifying one or more top predictors of recipient behavior, the one or more top predictors being selected from among the plurality of predictors based on preferred recipient behaviors, generating, for each of the one or more recipients and from the recipient data, one or more predictive scores for each combination of top predictor and recipient, and assigning, based on one or more predictive scores of a specific recipient, the specific recipient to a specific persona, wherein the specific persona describes an expected behavior of the recipient.

Abstract: A title standardization system is may be configured to detect an edit operation associated with the job title field of a member profile stored by an on-line social network system and, in response, perform operations to derive a canonical title that represents a raw title string found in the job title field. The derived canonical title may be then associated with the member profile, in which the originally-obtained subject title string was found. This association may be stored in a database for future use, e.g., for targeting job recommendations, recruiting, making professional contacts, as well as for other purposes.

Abstract: A method and system to suggest keywords to a social network member is described. A suggested keywords system, in one example embodiment, examines phrases that appear in profiles maintained by the on-line social networking system that are similar to the target profile and identifies those words and phrases that are most prominent in these profiles, utilizing discriminative strength values. These most prominent words and phrases may be presented to the target member as suggested keywords to be included in the member's professional summary.

Abstract: Systems, methods and a machine-readable media are described herein for a salary range engine to identify at least one attribute of a first member profile from a plurality of member profiles of a social networking service. The salary range engine correlates the at least one attribute with respect to at least a portion of trained salary data in a trained salary data repository. The salary range engine infers a target salary range based on a correlation between the at least one attribute of the first member profile and at least the portion of the trained salary data.

Abstract: In order to determine seniority associated with a title string associated with a member profile in an on-line social network system, a standardization system may be configured to operate as follows. A standardization system may determine a canonical title that corresponds to the title string, determine any seniority modifiers that may be present in the title string, and calculate a seniority value for the title sting as the sum of the seniority value assigned to the determined canonical title and the respective seniority values of the determined seniority modifiers. A seniority modifier is a phrase comprising one or more words that have been identified as being indicative of seniority if included in a title string.

Abstract: A title standardization system may be configured to automatically identify modifier terms in title strings and store these terms in a dictionary for future use. Modifier terms are those phrases in a title string that have been identified as indicative of a certain aspect related to the job of the associated member. In order to identify modifier terms, a title standardization system examines transitions between jobs that the members of the on-line social network system have reported via their respective profiles. If a transition pair comprising a first title string and a second title string was determined to be conforming to a stable pattern, a phrase that is included in the first title string and is lacking from the second title string is identified as a modifier phrase and stored in a dictionary for future use.

Abstract: A seniority standardization system may be configured to derive seniority values in the context of an on-line social network system. In order to determine a seniority rank of a given professional title, a seniority standardization system may leverage transition data, which is information that may be gleaned from a member profile with respect to the member's transition from one professional position to another. A seniority standardization system may also use time-based seniority signal. A time-based seniority value, which may be assigned to a particular professional title, is the amount of time that it typically takes to achieve a professional position represented by that particular professional title.

Abstract: Techniques for optimizing a guided edit process for editing a member profile page are described. According to various embodiments, profile edit task information associated with a member of an online social network service is accessed, the profile edit task information identifying one or more candidate profile edit tasks to be performed to update a member profile page of the member. Thereafter, if it is determined that the member recently completed a difficult profile edit task, a difficult candidate profile edit task is identified, and the member is prompted to perform the difficult candidate profile edit task. If it is determined that the member has not recently completed a difficult profile edit task, an easy candidate profile edit task is identified, and the member is prompted to perform the easy candidate profile edit task.