Comparison of MAE results for different algorithms with varying numbers of neighbors.

Comparison of MAE results for different algorithms with varying numbers of neighbors.</p

HLU results of different algorithms on various datasets.

HLU results of different algorithms on various datasets.</p

Flowchart of the algorithm.

Items and ratings serve as the input information for the algorithm, and the prediction results are generated through FPLV-ALL, FPLV-KNN, FPLV-SS, and FPLV-C methods.</p

Label vectors of items.

Item 1 includes labels 1, 2, 4, etc.; item 2 includes labels 1, 2, 3, etc.; and item 3 includes labels 2, 3, 4, 5, etc.</p

Comparison of F1 scores for different algorithms with varying numbers of neighbors.

Comparison of F1 scores for different algorithms with varying numbers of neighbors.</p

For relevant and irrelevant items, classify items into four categories based on whether the system makes a recommendation.

For relevant and irrelevant items, classify items into four categories based on whether the system makes a recommendation.</p

SA values of different algorithms when selecting different numbers of neighbors.

SA values of different algorithms when selecting different numbers of neighbors.</p

User similarity network before and after partitioning communities.

Node size and color in Fig 4(a) represent 6 categories based on node k values. In Fig 4(b), node size is categorized based on the k value, and node color is categorized into 27 communities. Larger nodes indicate higher k values in both figures. (a) User similarity network and (b) User similarity network with community information.</p

Variation in Item Degree Diversity (IDD) for different algorithms and neighbor selections.

Variation in Item Degree Diversity (IDD) for different algorithms and neighbor selections.</p

Comparison of prediction time for different algorithms on MovieLens-25M and Netflix datasets (unit: Minutes).

The black lines represent the upper and lower bounds of the distribution obtained from multiple trials in the five-fold cross validation. (a) MovieLens and (b) Netflix.</p