Exercise recommendation method and device based on typical degree and difficulty

Abstract

The invention discloses an exercise recommendation method based on the typical degree and difficulty. The method comprises the steps that exercise doing situations comprising the exercise number, difficulty and the passing rate of each target user on each type of exercises are calculated; according to feature vectors of the target users, the similarities between every two feature vectors of the target users are calculated, and many users with the highest similarity are selected for each target user as nearest neighbours; according to exercise doing situation of the nearest neighbour users, the scores of the target users for exercise not finished are predicted; a plurality of target exercises with the highest scores are selected as recommendation results of the target users. An exercise recommendation device comprises a calculating module, a first selecting module, a scoring module and a second selecting module. A traditional recommendation method is improved through the feature that exercises have the difficulty, and a good effect is obtained. The exercise recommendation method and device can be applied in a learning website in the future to help users to select learning content and formulate individualized learning schemes.

Description

technical field [0001] The invention relates to the fields of data mining, machine learning and information retrieval, and to the field of collaborative filtering recommendation, in particular to a topic recommendation method based on typicality and difficulty and a recommendation device thereof. Background technique [0002] At present, there are relatively mature recommendation systems and recommendation algorithms. The current mainstream recommendation algorithms are mainly divided into collaborative filtering recommendation (collaborative filtering, CF), content-based recommendation (content based, CB) and hybrid recommendation methods (hybrid methods). The hybrid recommendation algorithm combines the former two to achieve better results. [0003] In content-based recommender systems, items can be described as a series of vectors of attribute values, and the attribute values ​​describing the characteristics of items are called "content". The content-based recommendation...

AI Technical Summary

Problems solved by technology

A content-based recommendation system must be able to accurately describe the characteristics of the recommended items and match them with user preferences. When faced with data with ambiguous characteristics such as topics, it is difficult to describe them accurately, resulting in inaccurate recommendations.

The traditional collaborative filtering method does not need to accurately describe the items, but requires a large amount of scoring data. When it is applied to recommending knowledge and recommending topics, it is difficult to have enough scoring data

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