Personalized item-level vertical pagerank algorithm iRank

A vertical search engine, sorting algorithm technology, applied in computing, special data processing applications, instruments, etc., can solve problems such as insufficient effective information, no personalized optimization mechanism for returned results, and unstructured effective information.

Inactive Publication Date: 2011-11-09
倪毅
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The problems of traditional general search are becoming more and more prominent and deepening. The main problems are: too much invalid information (more noise data), insufficient effective information, unstructured effective information, and no personalized optimization mechanism for returned results
In the vertical field, the technology provides users with organized and structured object information, thereby greatly reducing the proportion of invalid information

Method used

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  • Personalized item-level vertical pagerank algorithm iRank
  • Personalized item-level vertical pagerank algorithm iRank
  • Personalized item-level vertical pagerank algorithm iRank

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0020] 1. Build an industry ontology library model.

[0021] The main task of constructing the ontology library model is to establish the structured information of each item object and the similarity matrix MII of item-item according to the database information.

[0022] Taking restaurants as an example, we summarize the following features for the feature vector of each item restaurant:

[0023] (name, cuisine, address, per capita consumption, discounts, reviews)

[0024] The above information is extracted from the crawled webpage through regular expressions and string matching algorithms, and an item restaurant object is constructed. This model uses feature to calculate the similarity between items. The specific calculation method is shown in formula (1).

[0025] ItemSim ( i , j ) = F ( i ) ...

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Abstract

The invention discloses a personalized item-level vertical pagerank algorithm iRank, and relates to a personalized item-level vertical search engine recommendation algorithm iRank. The basic idea of the algorithm is that: deep and detailed researches are made on the vertical field item-level pagerank algorithm and user behaviors, an industry ontology base model is constructed by collecting the information of a vertical field, user behavior information is collected by using a sentiment analysis technology and an Eyetrack technology for recording the eyetrack time of an Internet user in a current page in the field of data mining, a user interest model and a similar user model are constructed statistically, and finally the personalized recommendation algorithm based on the user interest model and the similar user model is combined in pagerank. The algorithm iRank can intelligently perform personalized ranking on item-level search engine semantic information retrieval results to influence rank ratings of returning items and realize inter-user personalized ranking on an item set result. Compared with the conventional pagerank algorithm (PageRank, HITS), the algorithm iRank improves the capability of a search engine in coping with user interests, and has a high practical application value.

Description

technical field [0001] The invention relates to algorithm research on the correlation ranking of personalized search results in the field of object-level vertical search engines. Background technique [0002] The development of the times has caused the scale of Internet pages to explode at an unimaginable speed, and the main contradiction of excess information and scarcity of attention has further deepened. The problems of traditional general search are becoming more and more prominent and deepening. The main problems are: too much invalid information (more noise data), insufficient effective information, unstructured effective information, and no personalized optimization mechanism for returned results. The development trend of the next-generation search engine is to be more intelligent, and the most important branch is object-level vertical search. In the vertical field, this technology can provide users with more relevant and effective information in the field than gener...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 倪毅张惟师
Owner 倪毅
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