In recommender systems, exploring user intents allows for a better understanding and exploration of user preferences, thereby improving recommendation performance. However, existing methods for modeling user intents often do so by statically setting the intent count, which can result in redundancy or insufficiency in capturing the full range of user intents. In order to solve this problem, this paper proposes a model named I ntent-aware Recommendation Based on P rincipal C omponent A nalysis (Intent PCA). Intent PCA is a novel application of PCA in the field of recommender systems. This model defines intents as users’ preferences for some specific relations shown on a knowledge graph, and constructs a user-relation matrix to calculate users’ preferences for relations. Then PCA is applied on the user-relation matrix to extract user intents. Benefit from good characteristics of PCA, our PCA-based user intent extraction model can comprehensively model user intents while simultaneously avoid intent redundancy. Moreover, by combining the user intents, this paper designs an intent-based information propagation method to differentially aggregate information from surrounding neighbor nodes. Experiments conducted on three datasets validate the effectiveness of the proposed Intent PCA model.

Naive Bayes (NB) continues to be one of the top 10 data mining algorithms due to its simplicity, efficiency and efficacy. Numerous enhancements have been proposed to weaken its attribute conditional independence assumption. However, all of them only focus on the raw attribute view, which is hard to reflect all the data characteristics in real-world applications. To portray data characteristics more comprehensively, in this study, we construct two label views from the raw attributes and propose a novel model called multi-view attribute weighted naive Bayes (MAWNB). In MAWNB, we first build multiple super-parent one-dependence estimators (SPODEs) as well as random trees (RTs), then we utilize each of them to classify each training instance in turn and use all their predicted class labels to construct two label views. Next, to avoid attribute redundancy, we optimize the weight of each attribute value for each class by minimizing the negative conditional log-likelihood (CLL) in each view. Finally, the estimated class-membership probabilities by three views are fused to predict the class label for each test instance. Extensive experiments show that MAWNB significantly outperforms NB and all the other existing state-of-the-art competitors.

In crowdsourcing scenarios, we can often obtain each instance's multiple noisy labels from different crowd workers and then use a label integration method to infer its integrated label. In spite of the effectiveness of label integration methods, a certain level of label noise still exists in integrated labels. To reduce the impact of label noise, noise correction has attracted much attention from researchers, and therefore a certain number of noise correction methods were proposed in recent years. Among them, Between-class Margin-based Noise Correction (BMNC) has demonstrated remarkable denoising performance. However, BMNC can only handle binary classification tasks. For multiclass classification tasks, we propose an effective but very simple noise correction method in this paper. We refer to our proposed method as Label Distribution-based Noise Correction (LDNC). At first, LDNC transforms each instance's multiple noisy labels into a label distribution. Then, LDNC uses the margin between the first- and second-largest label probabilities in the label distribution to identify and filter each possible noise instance and thus obtains a clean set and a noise set. Finally, LDNC builds a classifier on the clean set to relabel all instances in the noise set. The experimental results on 16 simulated and one real-world multiclass crowdsourced data sets show that LDNC significantly outperforms all the other existing state-of-the-art noise correction methods.

Due to its simplicity, efficiency, and efficacy, naive Bayes (NB) has continued to be one of the top 10 algorithms in the data mining and machine learning community. Of numerous approaches to alleviating its conditional independence assumption, feature weighting has placed more emphasis on highly predictive features than those that are less predictive. In this paper, we argue that for NB highly predictive features should be highly correlated with the class (maximum mutual relevance), yet uncorrelated with other features (minimum mutual redundancy). Based on this premise, we propose a correlation-based feature weighting (CFW) filter for NB. In CFW, the weight for a feature is a sigmoid transformation of the difference between the feature-class correlation (mutual relevance) and the average feature-feature intercorrelation (average mutual redundancy). Experimental results show that NB with CFW significantly outperforms NB and all the other existing state-of-the-art feature weighting filters used to compare. Compared to feature weighting wrappers for improving NB, the main advantages of CFW are its low computational complexity (no search involved) and the fact that it maintains the simplicity of the final model. Besides, we apply CFW to text classification and have achieved remarkable improvements.