Abstract
Current data has the characteristics of complexity and low information density, which can be called the information sparse data. However, a large amount of data makes it difficult to analyse sparse data with traditional collaborative filtering recommendation algorithms, which may lead to low accuracy. Meanwhile, the complexity of data means that the recommended environment is affected by multiple dimensional factors. In order to solve these problems efficiently, our paper proposes a multidimensional collaborative filtering algorithm based on improved item rating prediction. The algorithm considers a variety of factors that affect user ratings; then, it uses the penalty to account for users’ popularity to calculate the degree of similarity between users and cross-iterative bi-clustering for the user scoring matrix to take into account changes in user’s preferences and improves on the traditional item rating prediction algorithm, which considers user ratings according to multidimensional factors. In this algorithm, the introduction of systematic error factors based on statistical learning improves the accuracy of rating prediction, and the multidimensional method can solve data sparsity problems, enabling the strongest relevant dimension influencing factors with association rules to be found. The experiment results show that the proposed algorithm has the advantages of smaller recommendation error and higher recommendation accuracy.
Highlights
Recommendation algorithms are mainly divided into six categories: content-based filtering, collaborative filtering, recommendation based on association rules, recommendation based on utility, recommendation based on knowledge, and mixed recommendation [1, 2]
Based on the above calculations, this paper proposes a novel algorithm, namely, improved item-rating prediction (IIP) for user scoring. e main steps of IIP are shown in Figure 1(a). e basic idea is to form a set of error factors for each user through statistical learning, and apply it to the collaborative filtering algorithm to correct the item rating prediction
In order to verify the impact of a user’s scoring weight on the recommendation results and to prove that the recommendation accuracy of the collaborative filtering algorithm based on the user and improved item scoring is more accurate, it is necessary to compare our proposed algorithm with traditional algorithms that are based on classical item scoring prediction methods
Summary
Recommendation algorithms are mainly divided into six categories: content-based filtering, collaborative filtering, recommendation based on association rules, recommendation based on utility, recommendation based on knowledge, and mixed recommendation [1, 2]. Collaborative filtering (CF) algorithms are the most widely used and classic because of their easy implementation, high accuracy, and high recommendation efficiency. In the era of big data, one typical feature is that the amount of data is huge but the information density is low, which can be called information sparse data. Collaborative filtering algorithms are often ineffective when dealing with large amounts of sparse data. The complex data environment results in many factors affecting the recommendation. With the development of the mobile Internet, mobile devices can obtain more information about dimensions, such as location, weather, and social relationships. Most of the current collaborative filtering algorithms are based on a single dimension for recommendation
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