User comment-based product search method and system

Abstract

The invention discloses a user comment-based product search method. According to an information requirement provided by a user, a most related product list is searched by the method through combining product data and is returned to the user; the method comprises the following steps: analyzing the product data to obtain an index database, an affective characteristic database and a comment weight database; performing preprocessing and lexical item expansion on a query string submitted by the user to obtain a query lexical item set; searching products and obtaining final score values thereof; ordering from high to low according to the final score values of the products, and cutting off to obtain the product list. By adopting the method, the search effect can be optimized by the product comment information of the user; meanwhile the validity of the introduced information is ensured by analyzing the reference degree in a comment text; in addition, the production search application range and types queried by the user can be expanded; the method is suitable for the applications of product search, gift recommendation and the like of E-business websites.

Description

technical field [0001] The invention relates to the field of information retrieval, in particular to a product retrieval method and a product retrieval system based on user comments. Background technique [0002] When a user searches for a product, it retrieves the most relevant product list from the product library according to the information requirements provided by the user and returns it to the user. The prior art mainly adopts a facet-based retrieval method and a keyword-based retrieval method. [0003] Among them, the facet-based retrieval method takes each structured information of a product as a facet and divides it into several categories. The user's query is segmented, and then the product category corresponding to the query is judged and retrieved in the form of filtering. For example, for the query "black mobile phone around 2000", the system will determine that the demand object is "mobile phone", the range of "price" is [1500, 2500], and the "color" is "blac...

AI Technical Summary

Problems solved by technology

This method also has shortcomings. On the one hand, the user's comment text is not suitable for direct keyword retrieval, because the quality and credibility of the comment text are different, and they need to be treated differently; on the other hand, the keywords of the product description Information does not necessarily come from a product itself, and some global equivalence class information will be missed under the existing retrieval framework

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