Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and execute functions comprising: storing historical session data pertaining to user sessions and archived search queries submitted by users to a search engine; analyzing the historical session data to identify ambiguous queries, including semantically ambiguous queries and multi-language queries; monitoring search queries submitted to the search engine to detect the ambiguous queries; and in response to detecting an ambiguous query, generating a query resolution interface that displays categorical groupings, each of which corresponds to a possible intention of the ambiguous query. Other embodiments are disclosed herein.

Abstract: A search engine provides diverse search results in response to a search query using a trained diversity ranker. The diverse results may be generated by determining ranking positions of at least some search results based on delta feature values that are dependent on higher-ranked results. Delta feature values indicate a degree or magnitude of difference between a particular result and other results with respect to a particular feature, such as price, category, or shipping type. In providing the ranked results for presentation to a user at a computing device, a diversity feature indicator for at least some results may be generated. The diversity feature is a feature that distinguishes a given result from other results. As such, the diversity feature indicator represents that diversity feature for the particular result being different from other results and distinguishes the diversity feature from other features in the particular result.

Abstract: A systems including 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, perform: receiving in-session user activity information, the in-session user activity information comprising a user search query from a user and a ranked list of products to be displayed to the user based on the user search query, and a product of the products being part of a boost category; determining a respective content model prediction score for each of the products of the ranked list of the products; determining a respective discounted cumulative gain (DCG) score for each of the products of the ranked list of the products; analyzing the product that is part of the boost category to determine if the product is to be repositioned within the ranked list of the products based on the respective DCG score for each of the products; and in response to determining that the product that is part of the boost category is to be repositione

Abstract: This application relates to apparatus and methods for training machine learning models, and applying the trained models to determine an item's relevance to a search query. In some examples, features are generated based on item engagement data associated with a search query. First values are generated for each of a plurality of items based on the search query, a first attribute of each of the items, and the item engagement data. Second values are generated for the items based on the search query, the first attributes, a second attribute of each item, and the item engagement data. Third values are generated for the items based on the search query, the first attributes, a third attribute of each item, and the item engagement data. Features are generated based on the first, second, and third values, and a machine learning model is applied to the features to rank items.

Abstract: A result ranking system can include a computing device that is configured to obtain a plurality of items based on a query and a value corresponding to each item of the plurality of items. The computing system can also be configured to retrieve a value distribution corresponding to the query and generated based on customer interaction data. The computing system can also, for each item of the plurality of items, determine a feature value based on the value distribution and the corresponding value of the item. The computing system can be further configured to transmit the plurality of items to a customer device for display in an arrangement based on the corresponding feature value.

Abstract: This application relates to apparatus and methods for training machine learning models, and applying trained machine learning models to generate item ranking values. In some examples, user session data for multiple users is received. Based on the user session data, user engagement data is generated characterizing engagements of corresponding items for a search query. Further, a number of examines is determined for each of the corresponding items. The user engagement data for each item is normalized based on the corresponding number of examines, and ranking data is generated based on the normalized user engagement data. The ranking data characterizes a ranking of at least a subset of the items. Further, a machine learning model is trained based on the ranking data. In some examples, the trained machine learning model is applied to a query to generate a ranking of items, and the ranking data is transmitted to a web server.

Abstract: Systems and methods to identify a filter set in a keyword query are described. The system receives a query from a client machine. The system identifies filter sets based on the query and a based on rules. The filter sets include a first filter set that includes a first filter. The rules are utilized to associate at the least one keyword from the query to the first filter. The system further scores the filter sets based on probabilities to generate scores. The probabilities describe occurrences of attribute-value pairs in listings that respectively describe items that were previously transacted on a network-based marketplace. The system further identifies the first filter set from the filter sets based on the scores, generates a user interface including search results that are identified based on the identified first filter set, and communicates the user interface, over the network, to the client machine.

Abstract: The present disclosure is directed to apparatuses, systems, and methods for enhancing search results based on recent user interactions. As described herein, embodiments may infer various refinements for search queries; these refinements are based, at least in part, on the user's recent interactions with the search platform (e.g., within a current session). In other words, as the user is interacting with the search platform, one or more refinements may be inferred to help make the search results more relevant to the user.

Abstract: A computer-implemented system and method for providing information tagging in a networked system is disclosed. The apparatus in an example embodiment includes a tag engine configured to process a database of categorized product listings; to receive a user-provided tag associated with at least one of the product listings; to retain the user-provided tag; and to serve the user-provided tag to a user viewing at least one of the product listings.

Abstract: A search engine provides diverse search results in response to a search query using a trained diversity ranker. The diverse results may be generated by determining ranking positions of at least some search results based on delta feature values that are dependent on higher-ranked results. Delta feature values indicate a degree or magnitude of difference between a particular result and other results with respect to a particular feature, such as price, category, or shipping type. In providing the ranked results for presentation to a user at a computing device, a diversity feature indicator for at least some results may be generated. The diversity feature is a feature that distinguishes a given result from other results. As such, the diversity feature indicator represents that diversity feature for the particular result being different from other results and distinguishes the diversity feature from other features in the particular result.

Abstract: In various example embodiments, a system and method for enhancing autocomplete search suggestions are presented. The system receives a query portion with a token portion from a client device and generates a first search query suggestion set having a first order based on the token portion. The system accesses a token pool associated with the client device and generates a second search query suggestion set having a second order based on the token portion and the token pool. The system selects a first subset of search query suggestions and a second subset of search query suggestions. The system merges the first subset of search query suggestions and the second subset of search query suggestions into a third search query suggestion set, organizes the third search query suggestion set into a third order distinct from the first and second orders, and causes presentation of the third search query suggestion set.

Abstract: Systems and methods to seed a search are described. The system identifies a seed listing included in listings that describe items being offered for sale on a network-based marketplace. The system identifies a seed filter context based on the seed listing and probabilities. The probabilities describe occurrences of attribute-value pairs in a plurality of listings that respectively describe items that were previously transacted on the network-based marketplace. The system extracts values from the seed listing based on the seed filter context. The system initializes the seed filter context based on the values and generates search results based on the seed filter context. The search results include a second plurality of listings that are identified from the first plurality of listings. Finally, the system communicates interface information to a client machine including the seed filter context and at least one listing from the second plurality of listings.

Abstract: Methods and systems are provided to identify and present filters. The system receives a query, over a network, from a client machine and identifies a filter context based on the query. The filter context including a first plurality of filters. The system further identifies a second plurality of filters responsive the receiving the query. The identifying the second plurality of filters is based on the filter context and a plurality of probabilities. The system generates a user interface including search results that are identified based on the filter context. The system communicates the user interface, over the network, to the client machine.

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: The present disclosure is directed to apparatuses, systems, and methods for enhancing search results based on recent user interactions. As described herein, embodiments may infer various refinements for search queries; these refinements are based, at least in part, on the user's recent interactions with the search platform (e.g., within a current session). In other words, as the user is interacting with the search platform, one or more refinements may be inferred to help make the search results more relevant to the user.

Abstract: The present disclosure is directed to apparatuses, systems, and methods for enhancing search results based on recent user interactions. As described herein, embodiments may infer various refinements for search queries; these refinements are based, at least in part, on the user's recent interactions with the search platform (e.g., within a current session). In other words, as the user is interacting with the search platform, one or more refinements may be inferred to help make the search results more relevant to the user.

Abstract: Aspects of the present disclosure include a system comprising a computer-readable storage medium storing at least one program and a computer-implemented method for providing alternative recommendations using search context. Consistent with some embodiments, the method may include receiving a search query and receiving a request regarding an item included in one or more search results providing in response to the search query. The method may further include determining an alternative item recommendation using the search query, and causing presentation of the alternative item recommendation.

Abstract: Aspects of the present disclosure include a system comprising a computer-readable storage medium storing at least one program and a computer-implemented method for providing alternative recommendations using search context. Consistent with some embodiments, the method may include receiving a user search query and receiving a request to view an item included in a set of search results retrieved in response to the search query. The method may further include determining an alternative item recommendation using the search query and attributes of the item, and causing presentation of the item and the alternative item recommendation in response to receiving the request to view the item.

Abstract: Systems and methods to identify a filter set in a keyword query are described. The system receives a query from a client machine. The system identifies filter sets based on the query and a based on rules. The filter sets include a first filter set that includes a first filter. The rules are utilized to associate at the least one keyword from the query to the first filter. The system further scores the filter sets based on probabilities to generate scores. The probabilities describe occurrences of attribute-value pairs in listings that respectively describe items that were previously transacted on a network-based marketplace. The system further identifies the first filter set from the filter sets based on the scores, generates a user interface including search results that are identified based on the identified first filter set, and communicates the user interface, over the network, to the client machine.

Abstract: Systems and methods to identify a filter set in a keyword query are described. The system receives a query from a client machine. The system identifies filter sets based on the query and a based on rules. The filter sets include a first filter set that includes a first filter. The rules are utilized to associate at the least one keyword from the query to the first filter. The system further scores the filter sets based on probabilities to generate scores. The probabilities describe occurrences of attribute-value pairs in listings that respectively describe items that were previously transacted on a network-based marketplace. The system further identifies the first filter set from the filter sets based on the scores, generates a user interface including search results that are identified based on the identified first filter set, and communicates the user interface, over the network, to the client machine.