Search engine user information demand satisfaction evaluation method capable of integrating multiple views and semi-supervised learning

Abstract

The invention relates to a search engine user information demand satisfaction evaluation method capable of integrating multiple views and semi-supervised learning. The method is divided into the following six stages: preprocessing data, training a subview satisfaction model, distributing a dummy tag for unlabeled data, training a user satisfaction model based on the multiple views and the semi-supervised learning, and carrying out evaluation. Through a semi-supervised learning method, a small quantity of labeled data and a great quantity of unlabeled data are used for improving the performance of an evaluation model, and a multi-view learning method is imported to overcome the problem that a traditional single-view based semi-supervised learning method is always caught in local optimum. The search engine user information demand satisfaction evaluation method has the beneficial effects: (1) under the condition of the small quantity of labeled data, the search engine user information demand satisfaction can be effectively evaluated; (2) the small quantity of labeled data and the great quantity of unlabeled data can be used for improving the evaluation performance of the user satisfaction model; and (3) a search process of the user can be independently described from angles of behaviors and time, and the model can be prevented from being caught into the local optimum through mutual learning.

Description

technical field [0001] The invention relates to the field of Internet information technology, in particular to a search engine user information demand satisfaction evaluation method integrating multi-view and semi-supervised learning. Background technique [0002] With the rapid development of knowledge economy and informatization construction, the scale of network information data is rapidly expanding. Massive information resources not only enrich people's information sources, but also cause troubles for people to obtain information. Search engines, with their increasingly precise and humanized Information retrieval service has become one of the main tools for users to access the World Wide Web to find and obtain resource information. At the same time, search engines need to continuously improve their algorithms and optimize their systems to meet the increasing information needs of users and the requirements for efficient and convenient access to information resources. The...

AI Technical Summary

Problems solved by technology

[0003] Traditional search engine quality evaluation indicators such as top n accuracy rate (Precisionatn, Pn), average accuracy rate (MeanAveragePrecision, MAP), normalized discounted cumulative return (normalizeDiscountedCumulativeGain, nDCG), etc. need to use a large amount of manually labeled data to evaluate the performance of search engines , but this kind of manual labeling needs to consume a lot of manpower and time resources, and it is difficult to carry out large-scale real-time

Semi-supervised learning can make the evaluation method automatically use a large amount of unlabeled data to assist in the learning of a small amount of labeled data. However, most traditional semi-supervised learning methods are based on single-view, that is, simply combine all sub-attribute sets in the data into one A single attribute set ignores the unique statistical properties of each sub-attribute, and it is easy to fall into local optimum when the training data is extremely scarce.

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