## 3 Computational Approach
### Algorithm Code
Figure 2: Recall comparisons for different neighborhood sizes using Margarin dataset. #### 4.3.2 Results of Delicious

This study found the optimized \(\lambda\) value by exploring the performance of hybrid weight for the three metrics. In the Delicious dataset, the best \(\lambda\) scores were found to be 0.0, 0.8, and 0.5 for precision, recall and F-measure, respectively. The performances of hybrid weight were better than those of the weight containing only one factor in the Delicious dataset as well. Figure 4 presents a comparison of the precision values with the basic CF across different neighborhood sizes. Hybrid weight and time-based weight showed higher values than basic CF overall. Tag was the same as or higher than basic, except when the neighboring group was 3, 6, or 15. On average, hybrid weight was higher than CF by 0.16%, and time weight was higher than basic CF by 0.27%. Figure 5 illustrates the recall results in relation to the basic CF across a range of neighborhood sizes. Hybrid weight, tag-based weight, and time-based weight were greater than basic CF overall. On average, hybrid weight was larger than basic CF by 0.27%, and time weight was larger than basic CF by 11.81%. Figure 4: Precision comparisons for different neighborhood sizes using the Delicious dataset. Figure 3: F-measure comparisons for different neighborhood sizes using Margarin dataset. for generating accurate recommendations. Tags might represent niche interests or may not have been used consistently across users, leading to weaker recommendations. The Delicious dataset further ratified the robustness of the hybrid approach, with the \(\lambda\) values indicating that a mix of tag and time weights was essential for optimal results in most cases. However, the distinct \(\lambda\) values for each metric underscore that different components might dominate under varying evaluation criteria. ## 5 Conclusions and Further Research

CTS, a popular social network service, is emerging as a crucial tool for categorizing vast web resources for easy searching and sharing. This research focuses on creating and testing a new CF approach to improve recommendation quality. By proposing a CF model that simultaneously considers both tag and time information for weight generation and similarity calculations, the study demonstrates that the hybrid method substantially enhances personalized recommendations compared to models that account for either time or tag alone. Across two distinct datasets, Margarin and Delicious, our evaluation found that the hybrid and time-based weight strategies consistently outperformed the basic CF in terms of precision, recall, and F-measure. Specifically, in the Margarin dataset, the hybrid weight strategy led to an improvement average of 0.77% in precision, 13.27% in recall, and 5.63% in F-measure, while the time-based weight showed respective increases of 0.49%, 11.81%, and 5.14%. For the Delicious dataset, the hybrid weight strategy led to improvements averaging 0.16% in precision, 0.27% in recall, and 5.63% in F-measure, and the time-based weight demonstrated increases of 0.27%, 11.81%, and 5.14%. The tag-based weight's performance, however, was inconsistent, excelling in certain scenarios and faltering in others. These results underscore the significance of appropriate weight selection tailored to specific datasets. Moreover, it was revealed that the time weight function works well to develop CF performance. The reflection ratio of tag and time differed depending on the dataset. However, the optimized \(\lambda\) was greater than 0.5 in both datasets. When the ratio of tags was higher than that of time to configure the hybrid weight, the recommendation performance was improved. The recommended performance was enhanced as the size of the neighborhood increased, but did not improve at a certain level. From a cost-benefit point of view, it seems efficient to perform big data analysis with a neighborhood of 30 or less and to make recommendations to users.