Abstract: The technology disclosed herein relates to identifying an aspect from a search query based on using a multipartite graph generated using past user behavior and a node embedding algorithm for determining vector representations of nodes of the multipartite graph. For example, nodes of the multipartite graph can include nodes for prior search queries, items or item listings associated with the prior search queries, and one or more of an aspect or category of the items or item listings. In embodiments, the multipartite graph has dynamic edges between the nodes for the prior search queries and the items or item listings. In embodiments, a query expansion is performed based on identifying the aspect using the multipartite graph and node embedding algorithm. In embodiments, search results are provided based on identifying the aspect and performing the query expansion. For example, one or more identified aspects can be provided as selectable options.

Abstract: A search system performs item retrieval using search query categorization that matches query intent. Category embeddings are generated for categories based on hierarchical data and search information. For instance, the category embeddings can be generated using information regarding hierarchical relationships between the categories, co-occurring relationships between categories identified from search information, and initial embeddings that encode query-related information for each category. Category clusters can be formed using the category embeddings. When a search query is received, one or more categories are identified from a category cluster and used for selecting search results for the search query.

Abstract: Various methods and systems for providing query result items using an item title demand model are provided. A query is received at a search engine. Based on receiving the query, an item title demand engine is accessed. The item title demand engine operates based on an item title demand model which uses token weights, representing skip probabilities of tokens in item titles, to determine title scores for result item titles for corresponding queries. Based on accessing the item title demand engine, one or more result item titles for the query are identified from items in an item database. An identified result item title is identified based on a title score determined using the item title demand model and a highest skip probability of a token in the result item title. The one or more result item titles are communicated to cause display of the one or more result item titles.

Abstract: Various methods and systems for providing query result items using an item title demand model are provided. A query is received at a search engine. Based on receiving the query, an item title demand engine is accessed. The item title demand engine operates based on an item title demand model which uses token weights, representing skip probabilities of tokens in item titles, to determine title scores for result item titles for corresponding queries. Based on accessing the item title demand engine, one or more result item titles for the query are identified from items in an item database. An identified result item title is identified based on a title score determined using the item title demand model and a highest skip probability of a token in the result item title. The one or more result item titles are communicated to cause display of the one or more result item titles.