Abstract: Content recommendation includes: determining a plurality of user behavior categories pertaining to a plurality of user behaviors by a plurality of users within a period of time, and a plurality of user behavior frequencies corresponding to the plurality of user behavior categories; determining whether the number of user behavior categories exceeds a preset category threshold; in the event that the number of user behavior categories exceeds the preset threshold, excluding one or more user behavior categories such that the number of remaining user behavior categories does not exceed the preset threshold; determining, based at least on the remaining user behavior categories and corresponding user behavior frequencies, user preference data comprising frequency distribution information of a plurality of pieces of content information targeted by user behaviors in the remaining user behavior categories; and recommending content information to a user based on the user preference data.

Abstract: Content recommendation includes: determining a plurality of user behavior categories pertaining to a plurality of user behaviors by a plurality of users within a period of time, and a plurality of user behavior frequencies corresponding to the plurality of user behavior categories; determining whether the number of user behavior categories exceeds a preset category threshold; in the event that the number of user behavior categories exceeds the preset threshold, excluding one or more user behavior categories such that the number of remaining user behavior categories does not exceed the preset threshold; determining, based at least on the remaining user behavior categories and corresponding user behavior frequencies, user preference data comprising frequency distribution information of a plurality of pieces of content information targeted by user behaviors in the remaining user behavior categories; and recommending content information to a user based on the user preference data.

Abstract: Content recommendation includes: determining a plurality of user behavior categories pertaining to a plurality of user behaviors by a plurality of users within a period of time, and a plurality of user behavior frequencies corresponding to the plurality of user behavior categories; determining whether the number of user behavior categories exceeds a preset category threshold; in the event that the number of user behavior categories exceeds the preset threshold, excluding one or more user behavior categories such that the number of remaining user behavior categories does not exceed the preset threshold; determining, based at least on the remaining user behavior categories and corresponding user behavior frequencies, user preference data comprising frequency distribution information of a plurality of pieces of content information targeted by user behaviors in the remaining user behavior categories; and recommending content information to a user based on the user preference data.

Abstract: Various embodiments of a method, system, and apparatus related to query based on vertical search are disclosed. In one aspect, a method of query based on vertical search receives a user query. The method obtains a first category model from a category model warehouse based on the user query to generate a first query result. The first category model includes one or more commodity categories that correspond to one or more keywords in the user query. The method also obtains one or more commodity categories corresponding to the user query from a commodity warehouse to generate a second query result. The method further generates a final query result by combining the first query result and the second query result.

Abstract: Various embodiments of a method, system, and apparatus related to query based on vertical search are disclosed. In one aspect, a method of query based on vertical search receives a user query. The method obtains a first category model from a category model warehouse based on the user query to generate a first query result. The first category model includes one or more commodity categories that correspond to one or more keywords in the user query. The method also obtains one or more commodity categories corresponding to the user query from a commodity warehouse to generate a second query result. The method further generates a final query result by combining the first query result and the second query result.

Abstract: Various embodiments of a method, system, and apparatus related to query based on vertical search are disclosed. In one aspect, a method of query based on vertical search receives a user query. The method obtains a first category model from a category model warehouse based on the user query to generate a first query result. The first category model includes one or more commodity categories that correspond to one or more keywords in the user query. The method also obtains one or more commodity categories corresponding to the user query from a commodity warehouse to generate a second query result. The method further generates a final query result by combining the first query result and the second query result.

Abstract: Content recommendation includes: determining a plurality of user behavior categories pertaining to a plurality of user behaviors by a plurality of users within a period of time, and a plurality of user behavior frequencies corresponding to the plurality of user behavior categories; determining whether the number of user behavior categories exceeds a preset category threshold; in the event that the number of user behavior categories exceeds the preset threshold, excluding one or more user behavior categories such that the number of remaining user behavior categories does not exceed the preset threshold; determining, based at least on the remaining user behavior categories and corresponding user behavior frequencies, user preference data comprising frequency distribution information of a plurality of pieces of content information targeted by user behaviors in the remaining user behavior categories; and recommending content information to a user based on the user preference data.