Alibaba Group Holding Limited

Xiangyu Gu

Wei Ning

Qile Fu

Zhonglin Zu

Zhou Wang

Embodiments of the present application relate to a method for recommending search phrases, a system for recommending search phrases, and a computer program product for recommending search phrases. A method for recommending search phrases is provided. The method includes obtaining subject terms and descriptive terms relating to the subject terms from title information, combining s subject term with a descriptive term to form a set of search phrases, calculating a first appraisal value for each search phrase, the calculating of the first appraisal value comprising multiplying term frequency of the search phrase with an inverse document frequency of the search phrase, determining a second appraisal value of each search phrase, combining the first appraisal value and the second appraisal value of each search phrase to obtain a publisher recommendation appraisal value for each search phrase, and selecting a recommended phrase.

Method and system for recommending search phrases

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.

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Hangzhou, China

Xiangyu Gu