Tencent Technology (shenzhen) Company Limited

Feng Jiao

Liang Wang

Xiang He

Yuekui Yang

Bin Wang

Wenlong Zhang

Lei Zeng

Qi Guo

Yanxiong Lu

Ziming Zhuang

Yukun Wang

Ye Wang

Conglei Yao

Xiaohui Chen

Jianxun Zhou

Qi Bian

Chunbo Liu

Methods, devices, and servers for friend recommendation are provided. A user association set of a target user is obtained. Original data of each associated user in the user association set is obtained. The original data include location relationship data, associated friend data, time relationship data, or combinations thereof, between each associated user and the target user. The original data of each associated user is screened to obtain feature data to form a feature collection for each associated user. A pre-configured N-Tree prediction model is used to process the feature collection for a prediction calculation to obtain an association-predicting value for each associated user. According to the association-predicting value of each associated user, a friend user for the target user from the user association set is determined and recommended to the target user.

Method, device, and server for friend recommendation

Methods and devices for adding hyperlink to text are disclosed: generating hyperlink word list and characteristic word list in advance, determining co-occurrence frequency with each hyperlink word; to each text X which to be added the hyperlink, word segmentation processing them respectively, extracting the hyperlink word occurred in the hyperlink word list and the characteristic word occurred in the characteristic word list from results of word segmentation, determining weights of each extracted hyperlink word and extracted characteristic word, getting final weights of each extracted hypertext link word according to the co-occurrence frequency of each extracted characteristic word and each extracted hyperlink word and the weights; descendingly sorting each extracted hyperlink word according to the final weights, adding hyperlink to first k hyperlink words, and K is positive integer. Applying the solution, it can improve the relativity of the added hyperlink and the text, and it is easy to implement.

Method, device, and computer storage media for adding hyperlink to text

Disclosed are a search method, a search device and a storage medium. The method comprises obtaining all relevant documents of information to be sought; calculating a relevancy between each relevant document and the information to be sought based on a word matching algorithm and a semantics matching algorithm; performing sequencing processing on all the relevant documents according to the relevancy obtained through calculation, and displaying a sequencing result. Further disclosed is a search device. The present invention comprehensively considers matching between words, and matching of the semantics relationship between words, obtains an accurate relevancy calculation result, provides an ideal search result to a user, and improves satisfaction of the user.

Search method, search device and storage medium

Provided is a user question processing method and system. The method includes: extracting first feature information from a user question; calculating the similarity between the first feature and second feature information of each of at least two websites; posting the question on at least one of the at least two websites according to the similarity. The solution of the embodiment can be applied to a website providing a question and answer service, and the website can post a received user question on another website related to the concerned field of the question, thereby enlarging the scope of the user information exchange.

User question processing method and system

A method and apparatus for automatically adding a tag to a document are provided. The method comprises: determining a plurality of candidate tag words corresponding to the document; determining a corpus comprising a plurality of texts; selecting commonly-used words from the corpus as characteristic words; determining, for each of the characteristic words and each of the candidate tag words, a probability for co-occurrence of the candidate tag word with the characteristic word; abstracting characteristic words from the document, and calculating a weight for each of the abstracted characteristic words; and calculating, in the corpus, a weighted probability for co-occurrence of each of the candidate tag words with all of the characteristic words abstracted from the document; selecting the candidate tag word with a high weighted co-occurrence probability as a tag word to be added to the document.

Method, apparatus, and computer storage medium for automatically adding tags to document

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Shenzhen, China

Feng Jiao