Abstract: In some embodiments, techniques described herein relate to a method including: receiving a user instruction at an application executed by an electronic device; generating, at a framework executed by a server, one or more feature groups based on the profile data; generating, at the framework, a data configuration file; generating, at the framework, a model configuration file; specifying, at the framework, a sequence of functionality steps for execution; generating, at the framework, a prediction score based on the sequence of functionality steps; saving and/or executing, at the framework, the sequence of functionality steps; and generating, at the electronic device, one or more outputs including, for example, a prediction based on the sequence of functionality steps.

Abstract: In some aspects, the techniques described herein relate to a method including: receiving, as input to a binary search process, a subject vector embedding and a class vector embedding, wherein the subject vector embedding is generated from a plurality of subject text strings and wherein the class vector embedding is generated from a class text string; generating a similarity score; determining that the similarity score is below a threshold value; splitting the plurality of subject text strings into a first new plurality of subject text strings and a second new plurality of subject text strings; receiving a new subject vector embedding, wherein the new subject vector embedding is generated from the first new plurality of subject text strings; and calling the binary search process recursively using the new subject vector embedding and the class vector embedding as input to the binary search process.

Abstract: In some aspects, the techniques described herein relate to a method including: receiving, as input to a binary search process, a subject vector embedding and a class vector embedding, wherein the subject vector embedding is generated from a plurality of subject text strings and wherein the class vector embedding is generated from a class text string; generating a similarity score; determining that the similarity score is below a threshold value; splitting the plurality of subject text strings into a first new plurality of subject text strings and a second new plurality of subject text strings; receiving a new subject vector embedding, wherein the new subject vector embedding is generated from the first new plurality of subject text strings; and calling the binary search process recursively using the new subject vector embedding and the class vector embedding as input to the binary search process.

Abstract: In some aspects, the techniques described herein relate to a method including: receiving, as input to a binary search process, a subject vector embedding and a class vector embedding, wherein the subject vector embedding is generated from a plurality of subject text strings and wherein the class vector embedding is generated from a class text string; generating a similarity score; determining that the similarity score is below a threshold value; splitting the plurality of subject text strings into a first new plurality of subject text strings and a second new plurality of subject text strings; receiving a new subject vector embedding, wherein the new subject vector embedding is generated from the first new plurality of subject text strings; and calling the binary search process recursively using the new subject vector embedding and the class vector embedding as input to the binary search process.

Abstract: A method and system for providing a data analytics platform that facilitates efficient feature delivery based on reusability of software modules are provided. The method includes receiving a job request that corresponds to a feature desired by a user; transforming the job request into a directed acyclic graph (DAG) that includes a set of operations; and constructing a software program that is configured to execute the set of operations included in the DAG. The transformation is performed by extracting a set of configuration instructions that respectively correspond to operations included in the set of operations from the job request. The construction of the software program is performed by retrieving software modules that are configured to execute operations included in the DAG from a library that stores a plurality of reusable software modules that respectively correspond to algorithm functions.

Abstract: A system comprising a computer-readable storage medium storing at least one program and a computer-implemented method for providing recommendations are presented. Consistent with some embodiments, the method may include accessing a selection of queries executed on a database of items. The selection of queries may be used to retrieve a plurality of items from the database. The plurality of items may be grouped into one of multiple clusters based on similarities between items. The method may further include receiving a recommendation request related to an item corresponding to a first cluster of the multiple clusters. A recommendation may be generated using a related item selected from the first cluster.

Abstract: A system comprising a computer-readable storage medium storing at least one program and a computer-implemented method for providing recommendations are presented. Consistent with some embodiments, the method may include accessing a selection of queries executed on a database of items. The selection of queries may be used to retrieve a plurality of items from the database. The plurality of items may be grouped into one of multiple clusters based on similarities between items. The method may further include receiving a recommendation request related to an item corresponding to a first cluster of the multiple clusters. A recommendation may be generated using a related item selected from the first cluster.

Abstract: In various example embodiments, a system and associated method to enhance a user experience in an online environment is provided. In one embodiment, the method includes receiving a request over a network from a user where the request includes keywords to be used in a search for one or more items; the results from the search being displayed in a webpage. A determination is made whether to track metrics related to user activities associated with the results from the search. Based on a determination that the user activities are to be tracked, determining factors based on the tracked metrics related to the user activities, calculating a predictive model using one or more processors based on the determined factors, and displaying an enhanced webpage where components in the enhanced webpage are based on the predictive model.