Abstract: Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a method for providing content. Search results responsive to a query are identified including a first search result in a top set of search results, the first search result associated with a first entity. A first eligible content item is identified for presentation along with the search results, the first eligible content item associated with the first entity. A combined content item is identified that is a combination of the first search result and first eligible content item and is to be presented as a search result responsive to the query. The combined content item is augmented including: identifying entities related to the first entity, identifying content items that are associated with the related entities, selecting at least one identified content item, and using content from the selected content items to augment the combined content item.

Abstract: In some implementations, a system may determine, based on a qualification model, a prediction output of an analysis of user information. The system may determine, based on a generator model, a plurality of counterfactual explanations associated with the prediction output and the user information. The system may cluster, according to a clustering model, the plurality of counterfactual explanations into clusters of counterfactual explanations. The system may select, based on a classification model, a counterfactual explanation from a cluster of the clusters of counterfactual explanations. The system may provide a request for feedback associated with the counterfactual explanation. The system may receive feedback data associated with the request for feedback. The system may update a data structure associated with the clustering model based on the feedback data and the counterfactual explanation to form an updated data structure. The system may perform an action associated with the updated data structure.

Abstract: A client application sends an application programming interface call to an interface of a content management system. The call specifies one or more content item search criteria. Sending the call causes the content management system to perform a lookup in a content item index to identify at least one content item that satisfies the one or more content item search criteria. Based on sending the call, the client application receives from the content management system a suggestion to attach the at least one content item to a text being displayed by at the computing system.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for content recommendation. A method for content recommendation includes determining a similarity between a first recommendation result and a second recommendation result for a content set. The first recommendation result and the second recommendation result are determined based on different recommendation techniques and respectively indicative of a recommendation degree for each content in the content set. The method further includes adjusting the second recommendation result using the similarity. In addition, the method further includes determining a target recommendation result for the content set based on the first recommendation result and the adjusted second recommendation result. In this manner, the accuracy and stability of the finally obtained recommendation results can be improved.

Abstract: A media server machine may be configured to provide media content within a datastream. This datastream may be provided to a media device that is configured to present the media content on a display. Also, this datastream may contemporaneously contain an “app-sync indicator” for the media content. The app-sync indicator is a data structure that signals the media device to launch an application on a companion device. By providing the app-sync indicator contemporaneously with the media content in the datastream, the launching of the application on the companion device may be synchronized with the media content. The app-sync indicator may specify the application to be launched. Also, the app-sync indicator may specify supplemental content to be presented by the launched application on the companion device.

Abstract: A computer-implemented method is disclosed for generating structured data representations from unstructured input text sequences through a neural network-based extraction pipeline. The method involves acquiring input text sequences and processing them with an extraction neural network to produce output text sequences. Each output text sequence includes embedded delimiter tokens that annotate specific text segments, mapping them to semantic categories defined within a structured schema. The annotated text segments encapsulate semantically relevant information extracted from the input sequence. The output text sequences are subsequently processed to generate structured data records.

Abstract: An information processing device includes: a specification unit for specifying a reaction of a driver of a vehicle to evaluation information that evaluates driving of the driver and that is output from an output unit while the vehicle is being driven; an estimation unit for estimating an output method of the evaluation information that is appropriate for the driver based on the reaction of the driver, the reaction being the reaction specified by the specification unit; and a changing unit for changing the output method of the evaluation information to be output from the output unit to the output method of the evaluation information that is estimated by the estimation unit.

Abstract: A method for facilitating recursive data refinement with self-documentation is provided. The method includes receiving, via a data ingestion component, raw data that is published from a networked repository; generating a conformed data set from the raw data based on a conformation parameter; retrieving, via a communication interface, a feature configuration; generating, in real-time, a featurized data set from the conformed data set based on the feature configuration; and publishing the featurized data set to the data ingestion component for further processing.

Abstract: Systems or methods for the selection of large language models (LLMs). A system receives a request from a service that hosts an application. The request is configured to be processed by an LLM to generate a response. The system applies a classification model to the request to determine the class of the request. The classification model is a language model trained to receive text and classify the text into a plurality of classes. The system selects an LLM from a plurality of candidate LLMs based in part on the determined class of the request and recommends the selected LLM to the application.

Abstract: Provided is a system for predicting a response of a user in a multi-user environment. The system includes a display engine configured to display a user interface (UI) on a display screen for inputting a message corresponding to received messages from one or more contacts, a first processing engine configured to generate a list of correlated candidate messages associated with an input message using received messages, a second processing engine configured to predict a set of correlated candidate messages relevant to the input message from the list of correlated candidate messages, and a third processing engine configured to generate a priority list of candidate messages including the predicted set of correlated candidate messages. The display engine displays, on the display screen, at least one of one or more appropriate candidate messages based on a priority associated with the appropriate candidate messages in the generated priority list of candidate messages.

Abstract: Methods and systems for calculating, storing, and allowing electronic searches for metadata associated with media titles. The metadata includes scores specific to each actor and each genre of a media title, where the scores indicate the degree to which an actor's performance in that title corresponds to a particular genre. Electronic searches for particular actors' performance in a specific genre thus return more accurate results that more correctly reflect how well the actors' performances reflect that genre.

Abstract: In one embodiment, a method includes accessing a first document, accessing a plurality of second documents, calculating a relevance score for each of the plurality of second documents indicating a degree of relevance of the second document to the first document using an encoder of a machine-learning model, selecting a subset of the second documents based on their corresponding relevance scores, generating a target document by using the machine-learning model to process the subset of second documents and their corresponding relevance scores, and updating parameters of the machine-learning model based on a comparison between the first document and the generated target document.

Abstract: A system that enables searching for representing listings for accommodation reservations on a map is described. The system receives, by a network site of a listing network platform, input comprising search criteria associated with a geographical region and identifies a plurality of listings matching the search criteria. The system identifies a first subset listings of the plurality of listings that is each associated with a respective location that is within a boundary associated with the geographical region and a second subset listings of the plurality of listings that is each associated with a respective location that is outside the boundary associated with the geographical region. The system visually distinguishes the first subset of listings from the second subset of listings on a map-based graphical user interface (GUI) that represents the plurality of listings matching the search criteria.

Abstract: Systems and methods for multi-modal representation learning are described. One or more embodiments provide a visual representation learning system trained using machine learning techniques. For example, some embodiments of the visual representation learning system are trained using cross-modal training tasks including a combination of intra-modal and inter-modal similarity preservation objectives. In some examples, the training tasks are based on contrastive learning techniques.

Abstract: A method for generating a cross-lingual textual semantic model includes: acquiring a set of training data that includes pieces of monolingual non-parallel text and pieces of bilingual parallel text; determining a semantic vector of each piece of text in the set of training data by inputting each piece of text into an initial textual semantic model; determining a distance between semantic vectors of each two pieces of text in the set of training data based on the semantic vector of each piece of text in the set of training data; determining a gradient modification based on a parallel relationship between each two pieces of text in the set of training data and the distance between the semantic vectors of each two pieces of text in the set of training data; and acquiring a modified textual semantic model by modifying the initial textual semantic model based on the gradient modification.

Abstract: Devices often receive content items to be saved for later presentation to a user. In some instances, the content of the message is analyzed to retrieve supplemental information from external data sources, such as a map to a location discussed in a message. However, such supplementing often fails to reflect the personal context of the content item to the user, who may not easily recall the associated personal context when the content item is later presented. Instead, for a content item to be saved, a device may evaluate personal data sources to identify the personal context of the content item to the user, and add personal context annotations to a content item record of the saved content item. Later, during a presentation of the content item to the user, the personal context annotations may be included as a reflection of the personal context of the content item to the user.

Abstract: An Online Machine Learning System (OMLS) including an Online Preprocessing Engine (OPrE) configured to (a) receive streaming data including an instance comprising a vector of inputs, the vector of inputs comprising a plurality of continuous or categorical features; (b) discretize features; (c) impute missing feature values; (d) normalize features; and (e) detect drift or change in features; an Online Feature Engineering Engine (OFEE) configured to produce features; and an Online Robust Feature Selection Engine (ORFSE) configured to evaluate and select features; an Online Machine Learning Engine (OMLE) configured to incorporate and utilize one or more machine learning algorithms or models utilizing features to generate a result, and capable of incorporating and utilizing multiple different machine learning algorithms or models, wherein each of the OMLE, the OPrE, the OFEE, and the ORFSE are continuously communicatively coupled to each other, and wherein the OMLS is configured to perform continuous online machi

Abstract: A system identifies a candidate time interval in programming media content for a playback of a media content along with the programming media content. The identification is based on an analysis of a request received from a client device. The media content is based on user intent information and a plurality of targeting parameters associated with a user. Based on a specified version of the programming media content currently played at the client device, the media content is modified. Personalized media content corresponding to the candidate time interval in the programming media content is generated based on the modified media content and one or more constraints associated with the user. The personalized media content is presented along with a modified display view of the programming media content at the candidate time interval.

Abstract: Methods and apparatus for providing query suggestions to a user based on one or more past queries submitted by the user. Candidate query suggestions responsive to a current query may be identified. A candidate query similarity measure may be determined for a given candidate query suggestion based on matching entities related to the given candidate query suggestion and the one or more past queries. In some implementations, the similarity measure of the given candidate query suggestion may be based on a comparison of current entities of the given candidate query suggestion that match entities of one or more past queries, to a group of the current entities that includes entities that do not match the entities of one or more past queries. In some implementations a ranking of the candidate query suggestions may be determined based on the similarity measure.

Abstract: A technique for arranging nodes on a landscape based on a viewpoint desired by a user is provided. One aspect of the present disclosure relates to a node processing apparatus for synthesizing and extracting feature quantities that meet the needs of a user analysis from a plurality of types of feature quantities assigned for each node of a node set, and the node processing apparatus includes a receiving unit configured to receive, from a user, a designation related to an arrangement of nodes selected from the node set on an analysis axis assumed by the user, and a node processing unit configured to synthesize and extract feature quantities based on the arrangement of the received designation.

Abstract: Some implementations are directed to generating a personal database entry for a user based on free-form natural language input formulated by the user via one or more user interface input devices of a computing device of the user. The generated personal database entry may include one or more terms of the natural language input and descriptive metadata determined based on one or more terms of the natural language input and/or based on contextual features associated with receiving the natural language input. Some implementations are directed to generating, based on one or more personal database entries of a user, output that is responsive to further free-form natural language input of the user. For example, one or more entries that are responsive to further natural language input of the user can be identified based on matching content of those entries to one or more search parameters determined based on the further input.

Abstract: A method of identifying and enabling a data marketplace system transaction may comprise receiving an enterprise user object map defining a dataset field name and storage location for a dataset to be migrated pursuant to a user data integration process modeled by a user with a plurality of visual elements, identifying a data marketplace domain classification for the dataset to be migrated based on the enterprise user object map, querying a data marketplace application programming interface (API) to determine that it provides a marketplace for datasets meeting the data marketplace domain classification, generating a dataset sale integration process for transmitting the dataset of the data marketplace domain classification selected by the user for sale from a storage location defined by the user data integration process to a location defined by the data marketplace API, and automatically transmitting the dataset of the data marketplace domain classification to a purchaser.

Abstract: A system for re-ranking retrieved matching images. The system comprises a processor configured to extract at least one QI global feature from query image by global feature extractor module, and extract plurality of QI local features for the query image by local feature extractor module, and then search and retrieve top-k reference images from plurality of reference images based on at least one QI global feature and at least one KRI global feature associated with top-k reference images by filtering module. The processor is configured to perform matching of the plurality of QI local features with the plurality of KRI local features associated with the top-k reference images by fine tuning module and generate matching distance by distance fusion module, wherein the top-k reference images are re-ranked based on the matching distance and generate confidence score for the top-n reference images by confidence score generation module.

Abstract: Provided are a map data processing method and apparatus, and a storage medium, which relate to the field of data processing technology and, in particular, to artificial intelligence technology, for example, computer vision, map technology, and intelligent transportation. A specific implementation solution includes: processing landform coverage data according to a type of surface species coverage and a data processing rule associated with the type of surface species coverage to obtain a surface species coverage effect map; and generating a landform map according to the surface species coverage effect map and a reference map.

Abstract: A method and a computer system for determining the relevance of a text. The method includes: determining the similarity of the text to texts in a portfolio, where the text is compared with each of the texts in the portfolio as part of an individual comparison while determining a similarity value which indicates the similarity between the two texts in each case; assigning the similarity value to one of the two texts in the respectively performed individual comparison which was published at an earlier time or was first captured by a capture system at an earlier time; calculating a relevance value from the similarity values which were determined in the individual comparisons and were assigned to the text, and storing the calculated relevance value and/or transmitting the calculated relevance value to a communication end system via a computer network.

Abstract: An autonomous vehicle includes a sensor detecting an object in a vicinity of the autonomous vehicle, a processor coupled to the sensor to receive sensor data from the sensor, wherein the processor is configured to process the sensor data to recognize that the object is a pedestrian or cyclist. The processor cooperates with the sensor to track the pedestrian or cyclist. The processor determines if the pedestrian or cyclist satisfies a notification criterion and, upon determining that the pedestrian or cyclist satisfies the notification criterion, the processor outputs an illumination signal. A light projector receives the illumination signal and projects a beam of light onto a portion of a road on which the pedestrian or cyclist is currently situated to visually notify the pedestrian or cyclist that the autonomous vehicle is presently tracking the pedestrian or cyclist.

Abstract: Method(s), apparatus and system(s) are provided for entity type identification and/or disambiguation of entities within a corpus of text the method including: receiving one or more entity results, each entity result comprising data representative of an identified entity and a location of the identified entity within the corpus of text; identifying an entity type for each entity of the received entity results by inputting text associated with the location of said each entity in the corpus of text to a trained entity type (ET) model configured for predicting or extracting an entity type of said each entity from the corpus of text; and outputting data representative of the identified entity type of each entity in the received entity results.

Abstract: An example operation may include one or more of connecting, by a sparse peer, to a blockchain network of a plurality of peers controlled by orderers, selecting, by the sparse peer, a subset of data to be replicated, specifying, by the sparse peer, data selection filters based on the selected subset of the data, providing, by the sparse peer, the selection filters to the plurality of the peers, receiving, by the sparse peer, blocks matching the selection filters over a gossip protocol from the plurality of the peers, constructing, by the sparse peer, a local ledger of the sparse peer from the received blocks, and bootstrapping the sparse peer based on the local ledger.

Abstract: The present disclosure is directed towards systems and methods for detecting deviations between documents and portions thereof, extracting information from text and detecting deviations between obligations. Deviations are detected by splitting sentences in two documents apart, matching sentences from the different documents and detecting deviations between the matched sentences.

Abstract: A method for providing one or more random sample documents from a corpus of documents using a search engine is provided. The providing of each of the random sample documents comprises selecting randomly a time window from a set of time windows. A search query is sent to the search engine defining a search for documents of the corpus with time-stamps within the time window defined by the randomly selected time window. In response to the sending of the search query, a search result is receiving from the search engine. The search result comprises a set of the documents of the corpus with time-stamps within the time window. One of the documents comprised by the received set of documents is then selected randomly.

Abstract: The disclosure relates to method and system recommending tuning of parameters to generate a data analytics model. The method includes identifying at a data pre-processing stage a pre-processing subset from an associated set of predefined pre-processing methods for a predefined objective. The method includes identifying at a feature selection stage a feature subset from an associated set of predefined feature selection methods for the predefined objective. The method includes identifying at a model training stage a training subset from an associated set of predefined model training methods for the predefined objective. The method further includes generating a plurality of data analytics tuples and selecting a data analytics tuple from the plurality of data analytics tuples. An output result of the data analytics tuple includes highest ranked results for the predefined objective.

Abstract: A system can include one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising: receiving, at a search engine, a user search query submitted by a user; generating, using a recall personalization model, a simulated query that supplements the user search query with a feature vector reflecting personalization preferences of the user; and generating, using the search engine, search results for the user search query based, at least in part, on the simulated query that accounts for the personalization preferences of the user. Other embodiments are disclosed herein.

Abstract: A system is provided for reranking. The system comprises a user device and one or more servers. The system is configured to receive a plurality of candidate lists, rerank the plurality of candidate lists based on page-level information and a format of a recommendation page, generate recommendation results based on the reranked lists, and send the recommendation results to the user device. Each candidate list comprises a plurality of candidate items. The page-level information comprises interactions between the candidate items in each candidate list and between different candidate lists among the plurality of candidate lists. The reranking comprises using the format of the recommendation page to determine pairwise item influences between candidate item pairs among the candidate items in the candidate lists. The user device is configured to display the recommendation page with the recommendation results from the one or more servers.

Abstract: A non-transitory computer-readable medium includes computer-executable instructions that cause a processing unit of a user device to receive a multi-link uniform resource locator (URL), wherein the multi-link URL is associated with a plurality of routing link URLs configured to route the user device to a plurality of corresponding application pages. The executable instructions cause the processing unit to receive user selection of the multi-link URL and rank the routing link URLs based on application installation data stored on the user device, wherein the application installation data indicates whether applications for the routing link URLs are installed on the user device. The executable instructions cause the processing unit to select a final routing link URL based on the ranking of the routing link URLs and access an application page associated with the final routing link URL.

Abstract: The present disclosure relates to systems and methods for parsing unstructured data with neural networks. In one implementation, a system for parsing unstructured data may include at least one processor and at least one non-transitory memory storing instructions that, when executed by the at least one processor, cause the system to: receive unstructured data; apply a classifier to the unstructured data to identify a type of the unstructured data; based on the identification, select a corresponding neural network; apply the selected neural network to the unstructured data to obtain structured data; and output the structured data.

Abstract: Described is a system and method for enabling visual search for information. With each selection of an object included in an image, additional images that include visually similar objects are determined and presented to the user.

Abstract: A noise management system analyzes messages from a sender to a plurality of recipients. The noise management system prepares a message frequency of a quantity of messages sent to a unique recipient over a period of time. The noise management system sends the message frequency with a noise rank to the client device for the recipient to review.

Abstract: As provided herein, a domain model, corresponding to a domain of an image, may be merged with a pre-trained fundamental model to generate a trained fundamental model. The trained fundamental model may comprise a feature description of the image converted into a binary code. Responsive to a user submitting a search query, a coarse image search may be performed, using a search query binary code derived from the search query, to identify a candidate group, comprising one or more images, having binary codes corresponding to the search query binary code. A fine image search may be performed on the candidate group utilizing a search query feature description derived from the search query. The fine image search may be used to rank images within the candidate group based upon a similarity between the search query feature description and feature descriptions of the one or more images within the candidate group.

Abstract: Metric time series (MTS) data objects stored within in-memory storage are marked as inactive in response to determining that no MTS data has been received for the MTS objects within a first predetermined time period. In response to determining that an MTS object has been inactive for longer than a second predetermined time period, the MTS data object is migrated from in-memory storage to on-disk storage. Queries directed to MTS objects are first run against MTS object data stored within in-memory storage, and then against MTS object data stored within on-disk storage. In this way, an amount of in-memory storage needed to store MTS objects may be minimized, while optimizing search performance.

Abstract: The present disclosure provides systems and methods for content quasi-personalization or anonymized content retrieval via aggregated browsing history of a large plurality of devices, such as millions or billions of devices. A sparse matrix may be constructed from the aggregated browsing history, and dimensionally reduced, reducing entropy and providing anonymity for individual devices. Relevant content may be selected via quasi-personalized clusters representing similar browsing histories, without exposing individual device details to content providers.

Abstract: A system, server device, and method are provided for managing document metadata. The method includes storing metadata in a graph separately from documents with which the metadata is associated and connecting metadata in the graph according to a hierarchy, with each node of the graph being classified based on metadata content. The method also includes assigning properties to edges in the graph connecting the nodes; receiving, via a communications module, an instruction to update the metadata; and dynamically updating at least one edge or node in the graph according to the instruction without requiring access to an associated document. The method also includes receiving, via the communications module, an inquiry related to a document; and responding to the inquiry, via the communications module, after accessing metadata associated with the document by navigating the graph.

Abstract: A computing device of a system is deployed within a communication network for processing environmental, social, and governance (ESG) reports. The computing device is configured to obtain an ESG report of an entity, receive a question in natural language regarding information contained in or inferable from the ESG report, convert the ESG report into page-wise pieces of text, determine an answer to the question based at least upon the page-wise pieces of text of the ESG report, determine a location of a snippet related to the answer in the page-wise pieces of text of the ESG report, generate a bounding box through which the snippet is highlighted in a page containing the snippet, crop the page based on the bounding box, and display a cropped image of the page.

Abstract: A search query and a concept comprising a feature are received from a user. Positive and negative item sets are identified, where items of the positive item set comprise the feature and items of the negative item set do not comprise the feature. A hyperplane located between the positive and negative item sets is generated using a machine learning model. A concept activation vector (CAV) orthogonal to the hyperplane is determined. The CAV is used to produce a modified search query vector. Based on comparing the modified search query vector with item vectors, a second set of search results is provided.

Abstract: Search-result explanation systems, methods, and computer-program products receive a user search query, expand the search query into a plurality of sub-queries, perform a database search using the expanded user search query, and determine which sub-queries of the plurality of sub-queries matched with a particular search result. Results from the database search are re-indexed in an index generated on-the-fly and in-memory, within which the results are searched using the sub-queries to determine matching fields and match types. A score is determined based on the type of match(es) with a particular search result based on one or more predefined weights and normalized using a denominator comprising a fictitious, on-the-fly record configured to receive a perfect score according to the received user search query. A user interface showing ranked results and explanations for the ranking, including a score for the result based on the expanded user search query.

Abstract: Systems and methods are disclosed for processing electronic content, such as text, videos, and images. According to certain embodiments, user interactions with electronic content may be tracked over a plurality of modalities, such as web pages, email, mobile applications, and social media. The tracked user interactions may include copy/paste events, explicit user highlighting, social sharing, and user voting. Key passages of electronic content may be identified based on the tracked user interactions and ranked against one another. Ranking of passages may be based, for example, on a raw or normalized score for the identified key passages. Alternatively, the ranking of a passage may be based on a ratio of user interactions with the passage to total views of the electronic text containing the passage. One or more of the identified key passages (e.g., the highest ranked passages) may be published to one or more applications.

Abstract: Techniques described herein elect how data is deduplicated in a storage system. A similarity hash signature for a data unit is calculated. A digest table is searched for a similarity hash signature within a predetermined distance of the similarity hash signature for the data unit. Based on the search, either a similarity hash signature or a strong hash signature of the data unit is added to the digest table.

Abstract: Implementations are described herein for recommending actions based on entity or entity type. In various implementations, a partial free-form natural language input may be received from a user at an input component of a computing device. The partial free-form natural language input may identify an entity without identifying a responsive action and may be directed by the user to an automated assistant that operates at least in part on the computing device. The partial free-form natural language input may be analyzed to identify the entity. Based on the identified entity, a plurality or superset of candidate responsive actions may be identified, filtered, and/or ranked based on one or more signals. The automated assistant may then provide output that recommends one or more of the candidate responsive actions based on the ranking and/or filtering.

Abstract: A method, apparatus and device used to search for content, and a computer-readable storage medium, which relate to the field of data processing. In the method, in response to receiving a search request for a target search term, obtain multiple historical search records related to multiple historical search requests, each historical search record comprising the historical search term targeted by the corresponding historical search request. Then, determine a first historical search record matching the target search term from the multiple historical search records. Furthermore, based on the relationship between multiple historical search records, determine from the multiple historical search records a second historical search record associated with the first historical search record. The method further comprises determining an extended result for the target search term on the basis of the search result corresponding to the second historical search record.

Abstract: Systems and methods are provided for determining an environmental impact of one or more transactions. An example method generally includes accessing transaction data representative of a plurality of transactions, where each of the transactions involves a user and a merchant, and accessing at least one index indicative of environmental impact of a plurality of merchants. The method also includes generating a graph based on the users and merchants included in the transaction data, whereby the graph is representative of the plurality of transactions, and determining a mapping between ones of the merchants involved in the transaction data and the environmental impact indicated by the at least one index, based on at least one of: the graph, the accessed at least one index, a graph convolution network (GCN), and a graph neural network (GNN). The method then includes publishing the mapping between the ones of the merchants and the environmental impact.

Abstract: A method implements a dataset rank metric for measuring dataset relevance. Metadata is identified for a plurality of datasets. A graph structure is generated in storage. The graph structure includes a multitude of nodes connected by a multitude of edges. Each node of the multitude of nodes representing a respective dataset of a multitude of datasets, the multitude of edges connecting the multitude of nodes according to a data lineage determined from metadata of the multitude of datasets. A composite score is generated for each node of the graph. The computer processor iteratively processes the composite scores for the multitude of nodes of the graph to generate a dataset rank for each dataset. The multitude of datasets is presented in an interface, sorted according to the respective dataset rank of each dataset.