Recommendation algorithm combining user comments and scoring information

Abstract

The invention discloses a recommendation algorithm combining user comments and scoring information. The recommendation algorithm is implemented by the following specific steps: constructing a probability generation model for discovering potential topic dimensions in a user comment text; constructing a recommendation objective function based on the combination of a user score matrix decomposition model and a topic discovery model; and realizing product recommendation prediction based on the user comment text and the scoring data through iterative computation of the target function. According tothe algorithm, the comment information of the user is fully considered, the potential theme distribution in the comment text is utilized, the user scoring data and the user comment text are combined,and the cold start problem in a recommendation system is effectively solved; and meanwhile, compared with a method of separately considering two data sources, the method can more accurately perform score prediction, and is particularly suitable for score prediction of new products and new users.

Description

Technical field: [0001] The invention relates to the field of recommendation algorithms, in particular to a recommendation algorithm combining user comments and scoring information. Background technique: [0002] Recommender systems are widely used in various online platforms, and it has changed the way users discover and evaluate products online. Existing recommendation methods can be divided into two categories: collaborative filtering methods and content-based recommendation methods. The collaborative filtering method is based on explicit ratings and other information to model, although it can obtain a better recommendation effect, but there is a problem of sparsity of rating data. The content-based recommendation method recommends by mining products with the same or similar attributes, and the recommendations produced by this method have the problem of single recommendation results. There are many studies on rating modeling, but the sparsity of rating data, the cold st...

AI Technical Summary

Problems solved by technology

Although a lot of research work is on ratings and user comment texts, they all study these two points in isolation, and few studies try to combine these two sources of information for recommendation algorithm research.

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