Abstract: A system and method of identifying suspicious item-related features are disclosed. In some embodiments, a new item listing is received. The item listing may correspond to a request to list an item for sale on an e-commerce website. Item-related data for the item listing may be extracted. The item-related data can be used by a model to classify the new item listing. The model may be trained on data comprising identifications of which item listings in the sample of item listings are suspicious and identifications of which item listings in the sample of item listings are not suspicious may be received.

Abstract: A system and method of identifying suspicious item-related features are disclosed. In some embodiments, a new item listing is received. The item listing may correspond to a request to list an item for sale on an e-commerce website. Item-related data for the item listing may be extracted. The item-related data can be used by a model to classify the new item listing. The model may be trained on data comprising identifications of which item listings in the sample of item listings are suspicious and identifications of which item listings in the sample of item listings are not suspicious may be received.

Abstract: A system and method of identifying suspicious item-related features are disclosed. In some embodiments, a sample of item listings is received. Each item listing in the sample of item listings may correspond to an item offered for sale on an e-commerce website. Item-related data for each item listing in the sample of item listings may be extracted. Training data comprising identifications of which item listings in the sample of item listings are suspicious and identifications of which item listings in the sample of item listings are not suspicious may be received. A model for classifying a candidate item listing on the e-commerce website as being suspicious may be determined using the training data.

Abstract: Example embodiments described herein disclose systems and methods for near-identical multi-faceted entity identification within search results from an online marketplace. The online marketplace may be or include a group of one or more server machines configured to provide one or more online marketplace services, including the near-identical multi-faceted entity identification system. A user device may accordingly request and receive, from the online marketplace, a set of item listings based on submitted search criteria. The online marketplace may then access the set of item listings and identify one or more similar item listings among the set of item listings in order to demote a ranking of the similar item listings within the set.

Abstract: A user may submit a search query to a search engine. The search engine may process the search query and generate a set of results. Each of the items searched by the search engine may have been pre-assigned to a category in a category tree. Previous interactions by other users with the items after similar queries may have been recorded. The search engine may identify categories based on the distribution of the interacted-with results among the categories. The category tree may be analyzed at different levels, based on the entropy observed at each level. A level with low entropy may be chosen, and categories at that level used to constrain the query.

Abstract: A user may submit a search query to a search engine. The search engine may process the search query and generate a set of results. Each of the items searched by the search engine may have been pre-assigned to a category in a category tree. Previous interactions by other users with the items after similar queries may have been recorded. The search engine may identify categories based on the distribution of the interacted—with results among the categories. The category tree may be analyzed at different levels, based on the entropy observed at each level. A level with low entropy may be chosen, and categories at that level used to constrain the query.

Abstract: A user may submit a search query to a search engine. The search engine may process the search query and generate a set of results. Each of the items searched by the search engine may have been pre-assigned to a category in a category tree. Previous interactions by other users with the items after similar queries may have been recorded. The search engine may identify categories based on the distribution of the interacted-with results among the categories. The category tree may be analyzed at different levels, based on the entropy observed at each level. A level with low entropy may be chosen, and categories at that level used to constrain the query.

Abstract: Example embodiments described herein disclose systems and methods for near-identical multi-faceted entity identification within search results from an online marketplace. The online marketplace may be or include a group of one or more server machines configured to provide one or more online marketplace services, including the near-identical multi-faceted entity identification system. A user device may accordingly request and receive, from the online marketplace, a set of item listings based on submitted search criteria. The online marketplace may then access the set of item listings and identify one or more similar item listings among the set of item listings in order to demote a ranking of the similar item listings within the set.

Abstract: A system and method of query expansion are disclosed. A query expansion source, a query expansion candidate, and feature data for the query expansion source and the query expansion candidate are received. The feature data comprises information for a plurality of features. A determination is made as to whether the query expansion candidate qualifies as an expansion of the query expansion source based on an analysis of the information for the plurality of features. The query expansion candidate is assigned as an expanded query of the query expansion source in a query expansion dictionary in response to a determination that the query expansion candidate qualifies as an expansion of the query expansion source.

Abstract: A system and method of identifying suspicious item-related features are disclosed. In some embodiments, a sample of item listings is received. Each item listing in the sample of item listings may correspond to an item offered for sale on an e-commerce website. Item-related data for each item listing in the sample of item listings may be extracted. Training data comprising identifications of which item listings in the sample of item listings are suspicious and identifications of which item listings in the sample of item listings are not suspicious may be received. A model for classifying a candidate item listing on the e-commerce website as being suspicious may be determined using the training data.

Abstract: A user may submit a search query to a search engine. The search engine may process the search query and generate a set of results. Each of the items searched by the search engine may have been pre-assigned to a category in a category tree. Previous interactions by other users with the items after similar queries may have been recorded. The search engine may identify categories based on the distribution of the interacted-with results among the categories. The category tree may be analyzed at different levels, based on the entropy observed at each level. A level with low entropy may be chosen, and categories at that level used to constrain the query.

Abstract: A user may submit a search string to a system. Before processing the search, the system may analyze the search string and modify it. For example, the search string may be modified by treating some terms as a phrase, by dropping some terms, by treating some terms as attributes, or any suitable combination thereof. The modification of the search string may be based on an analysis of prior search strings and user actions. The results of a search based on the modified search string may be returned to the user.

Abstract: A system and method of query expansion are disclosed. A query expansion source, a query expansion candidate, and feature data for the query expansion source and the query expansion candidate are received. The feature data comprises information for a plurality of features. A determination is made as to whether the query expansion candidate qualifies as an expansion of the query expansion source based on an analysis of the information for the plurality of features. The query expansion candidate is assigned as an expanded query of the query expansion source in a query expansion dictionary in response to a determination that the query expansion candidate qualifies as an expansion of the query expansion source.