Abstract: Systems are configured for generating and utilizing training data to train learn-to-rank type models in a manner that preserves privacy of client data used for generating the training data. The systems extract features and patterns of the user queries, search results and user interactions with the search results without tracking, storing or transmitting underlying values of the user data to preserve privacy of the user data. Systems are also configured to infer search result quality based on at least the user behavior data, and optionally query intentions, and to generate and label corresponding training data accordingly. This training data is applied to learn-to-rank type models to train the learn-to-rank type model to improve search quality of search results provided by the learn-to-rank type models when new user queries are processed that having features and patterns corresponding to the filtered and labelled training data.

Abstract: A textual search index is used to identify data objects that have numerical properties that satisfy search queries. As described herein, a search index contains search index entries, each of which specifies a reference string and a set of data object identifiers. When an original search query is received, the search query is transformed into a transformed search query such that numerical comparison query elements in the original search query are transformed into equivalent textual comparison query elements. The search index is then used to identify ones of the data objects that satisfy the transformed search query. A list of the identified data objects may then be provided in response to the original search query.

Abstract: A textual search index is used to identify data objects that have numerical properties that satisfy search queries. As described herein, a search index contains search index entries, each of which specifies a reference string and a set of data object identifiers. When an original search query is received, the search query is transformed into a transformed search query such that numerical comparison query elements in the original search query are transformed into equivalent textual comparison query elements. The search index is then used to identify ones of the data objects that satisfy the transformed search query. A list of the identified data objects may then be provided in response to the original search query.

Abstract: A textual search index is used to identify data objects that have numerical properties that satisfy search queries. As described herein, a search index contains search index entries, each of which specifies a reference string and a set of data object identifiers. When an original search query is received, the search query is transformed into a transformed search query such that numerical comparison query elements in the original search query are transformed into equivalent textual comparison query elements. The search index is then used to identify ones of the data objects that satisfy the transformed search query. A list of the identified data objects may then be provided in response to the original search query.

Abstract: A textual search index is used to identify data objects that have numerical properties that satisfy search queries. As described herein, a search index contains search index entries, each of which specifies a reference string and a set of data object identifiers. When an original search query is received, the search query is transformed into a transformed search query such that numerical comparison query elements in the original search query are transformed into equivalent textual comparison query elements. The search index is then used to identify ones of the data objects that satisfy the transformed search query. A list of the identified data objects may then be provided in response to the original search query.