Personalized commodity recommendation method based on frequency matrix and text similarity

Abstract

The invention discloses a personalized commodity recommendation method based on a frequency matrix and text similarity. The method comprises the following steps: data acquisition, data cleaning, access user identification, session identification and transaction identification are carried out during pre-processing to obtain data of unified format; and the personalized commodity recommendation method based on a frequency matrix and text similarity is used to perform calculation to obtain a commodity candidate set and perform grading on the basis of the commodity candidate set, and a final result is presented to a user. A commodity recommendation module is constructed and implemented by the use of an access frequency matrix and text similarity calculation, the complexity of a recommendation system is reduced as far as possible, the requirement for real-time recommendation is met, and high coverage rate and high matching rate are maintained.

Description

technical field [0001] The invention relates to e-commerce technology, in particular to a personalized product recommendation method based on frequency matrix and text similarity. Background technique [0002] In the modern information service environment, users' information needs are increasingly diversified and personalized, and there are obvious personality differences among different users. With the continuous enrichment of network resources and the continuous expansion of network information, people's dependence on the network is getting stronger and stronger. However, it is not easy to obtain the required information from the Internet. Although various search engines play an extremely important role, they cannot meet the individual needs of users. It can be seen that the diversification of information and its dissemination creates demand for personalized information services, and also brings greater complexity and difficulties. The idea of ​​personalized service has ...

AI Technical Summary

Problems solved by technology

However, these methods have many shortcomings: the content-based recommendation algorithm lacks personalization, and can only find items that users are interested in, but cannot find new products that users will be interested in in the future; Analysis, but in many cases, attributes do not reflect some hidden features; lack of user feedback; recommendation technology based on user statistics is useful in some websites with membership as the main sales model, but it is not applicable to ordinary e-commerce model; in fact, the recommendation based on knowledge and utility has a common feature with the recommendation based on content, which is the need to describe the characteristics of the item, that is, the recommended product, and then recommend

However, because the association rules do not consider the order of the items in the rules, and users visit the website in strict order, there are certain deficiencies in the recommendation technology based on association rules.

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