Individual service recommendation system and method based on topic model

Abstract

The invention provides an individual service recommendation system based on a topic model. The individual service recommendation system comprises a social data acquisition module, a topic model construction and analysis module, a user interest modeling module, a service recommendation module, a user interaction module and a system management control module. According to the system, data is acquired by using a social network platform, the interests of a user are mined and modeled through the topic model, and the user interests are mined semantically, so that the problems of high dimension, data sparseness and the like of the conventional interest model based on a vector space model are solved; meanwhile, the long-term interests and short-term interests of the user are considered, the user interests can be reflected more truly, and accurate service recommendation specific to user interests is provided for the user.

Description

technical field [0001] The invention relates to the application field of computer technology, in particular to a personalized service recommendation system and method based on a theme model. Background technique [0002] With the development of information technology, especially network technology, the scale of information system is getting bigger and bigger, the amount of information is also increasing, the tools and methods of obtaining information are becoming more and more abundant, and more and more information is provided to users through the network. At the same time, the structure and content of information systems are becoming more and more complex. In the mass information, it is difficult to accurately describe the required information characteristics, and users are often helpless in the face of a large amount of information, and get lost in a large amount of information space; at the same time, the retrieval methods provided by various information systems often ca...

AI Technical Summary

Problems solved by technology

[0007] The above-mentioned technologies have the following problems: First, the methods based on collaborative filtering all require user rating tables, and there is a "cold start" problem, that is, the recommendation results are initially inaccurate due to insufficient known information. Only when the evaluation information increases, the recommendation results can be gradually improved

In addition, as the system scale increases, data sparsity defects appear, which leads to the degradation of recommendation service quality.

At the same time, if no user has ever commented on a product, the product cannot be recommended

Second, based on the content filtering method, the ability to describe the recommended items is limited, and users are often required to provide user data information, while ignoring the use of users' social network tools as a source of mining user interests, and unable to discover new interesting things for customers. resource

Third, when mining user interests, the traditional vector space model or language model simply considers the similarity of documents in the word space, and has never carried out interest mining from the semantics, which is more accurate

Fourth, when performing personalized related recommendations in the above-mentioned disclosed technologies, they often pay attention to the similarity and ignore the novelty of the content, provide users with new information, and seldom consider the impact of user interaction feedback on the accuracy of recommended content

[0008] The present invention aims to design a personalized service recommendation system and method based on a topic model, which uses a social network platform to obtain data, mines and models user interests through a topic model, and mines user interests semantically, solving the problem of The traditional interest model based on the vector space model has high dimensionality and sparse data. At the same time, it takes into account the long-term and short-term interests of users, which can more truly reflect user interests and provide users with accurate service recommendations tailored to user interests.

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