## 3 Computational Approach
### Algorithm Code
The evolution of online content, as corroborated by the literature, showcases the need for advanced information filtering and recommendation techniques, an area which our research critically addresses. CTS, as a collaborative tool, has become invaluable in categorizing online resources, with the literature underscoring its adaptability from basic functionalities to more advanced uses in contemporary online communities, such as SNS and e-commerce [29; 30]. Further, tags' semantic importance has been pivotal in information extraction, leading to their incorporation in several recommendation systems [19; 33]. In line with the recent trends and findings from researchers such as [PERSON] et al. [34] and [PERSON] et al. [20], our study also converges on the theme of integrating tag information while also echoing [PERSON] [35]'s methodological approach, which considers user preferences and similarities. Our research thereby forms a synthesis of the most prominent concepts and methodologies found in the literature, channeling them into a cohesive, updated model. In our study, the synergy of tag and time empowered the model to intricately decipher evolving user predilections, highlighting not merely their choices, but the temporality of those choices, a feature pivotal in domains like e-commerce, media platforms, and social networks. Contrary to conventional models, which are more static, our system thrives on adaptability, harnessing real-time data, which ensures its resilience and aptness in the face of swift digital transformations. This innovative fusion yields a richer insight into user behaviors over traditional bookmark-centric models. Empirical analysis of both the Margarin and Delicious datasets reinforced this, with our hybrid model consistently eclipsing the performance of rudimentary CF. Moreover, the model adeptly sidestepschallenges inherent to solely tag-based or time-based systems, balancing the dynamism of tags with the contextuality of time, thereby underscoring its robustness and efficacy. The study's limitations and future directions encompass several avenues. First, the research solely relied on the Margarin and Delicious datasets. There are many other prominent social tagging platforms, including Facebook, Twitter, Instagram, and CiteULike. Hence, it is paramount for future endeavors to consider a wider array of datasets in order to augment the generalizability of the findings. Second, the study did not juxtapose the proposed algorithm with other existing methodologies. The primary objective was understanding the depth of insights that tag and time data can offer over and above basic bookmarking details. Comparing the proposed methodology with other recommendation algorithms will be a pertinent step in future research. Another dimension for further investigation is the differential impact of tag and time information on preference versus similarity. Exploring varied combinations of recommendation systems by independently measuring preference and similarity can open new doors. Moreover, future research should also delve into analyzing the effects and behaviors segmented by age group and gender. Such a demographic-based analysis may offer more nuanced insights into user interactions and preferences. Conceptualization, [PERSON], [PERSON]-h.H. and J.Y.C. ; methodology, H.J., J.-h.H. and J.Y.C. ; formal analysis, H.J. and J.-h.H. ; data curation, J.Y.C. ; writing--original draft, H.J. All authors have read and agreed to the published version of the manuscript. This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A5A2A03052219). Not applicable. The data used in this study are available from the corresponding authors upon reasonable request. The authors declare no conflict of interest. ## References

* [PERSON] and [PERSON] (2019) [PERSON]; [PERSON] The wisdom of amateur crowds: Evidence from an online community of sports tipsters. _Eur. J. Oper. Res._**2019**, _272_, 1073-1081. [CrossRef]
* [PERSON] et al.