Part-time job recommendation engine recall method combining big data offline calculation and real-time monitoring

Abstract

The invention discloses a big data off-line calculation and real-time monitoring combined part-time job recommendation engine recall method, and the method comprises the following steps: dividing part-time jobs into a plurality of job group tags, and then dividing the job group tags into local part-time job tags and remote part-time job tags according to the positions of users and the part-time jobs; acquiring a data source of a part-time job, dividing the local part-time job label and the remote part-time job label into an hbase label and a redis label according to the data source, correspondingly storing the hbase label and the redis label in an hbase database and a redis database, and finally, configuring different job group labels for different scenes to recall corresponding part-time job data, and returning the part-time job data for a subsequent recommendation engine to perform calculation operation. Therefore, the number and comprehensiveness of recalled corresponding part-time job data can be guaranteed, the calculation requirement of a recommendation engine can be met, and the recommendation effect of the recommendation engine is guaranteed.

Description

technical field [0001] The invention relates to the application field of part-time job recommendation, in particular to a part-time job recommendation engine recall method combined with big data off-line calculation and real-time monitoring. Background technique [0002] At present, the recruitment industry is undergoing rapid development and transformation. On the one hand, with the emergence of the Internet and the wave of national entrepreneurship, various industries are facing industrial upgrading. The focus of enterprise competition is the competition for talents, and talent recruitment has become the top priority of each enterprise . Part-time jobs belong to the category of flexible employment, which is relatively special. Not only are there various job types and short work cycles, but the requirements for skills and experience are also very different from those of ordinary full-time jobs. Therefore, in order to make the best part-time job recommendation effect, it is...

AI Technical Summary

Problems solved by technology

The recall strategy is an important part of the entire big data job recommendation. Subsequent job filtering, sorting, and diversified interspersed display all need to rely on the result set of previous job recall. For example, when we recommend part-time jobs to users, each time the personality The computerized calculation and analysis are all based on the recalled positions. If the number of recalled positions in the early stage is insufficient or not comprehensive enough, the subsequent algorithms will not be able to perform well.

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