Modeling Cognitive Processes in Social Tagging to Improve Tag Recommendations
This work addresses the need for more cognitively informed tag recommendation systems for users of social bookmarking platforms, representing an incremental improvement over existing data-driven approaches.
The paper tackled the problem of tag recommenders lacking understanding of cognitive processes in social tagging by modeling individual and collective dynamics, resulting in a novel algorithm that outperforms state-of-the-art methods like Collaborative Filtering and FolkRank on datasets from BibSonomy, CiteULike, and Delicious.
With the emergence of Web 2.0, tag recommenders have become important tools, which aim to support users in finding descriptive tags for their bookmarked resources. Although current algorithms provide good results in terms of tag prediction accuracy, they are often designed in a data-driven way and thus, lack a thorough understanding of the cognitive processes that play a role when people assign tags to resources. This thesis aims at modeling these cognitive dynamics in social tagging in order to improve tag recommendations and to better understand the underlying processes. As a first attempt in this direction, we have implemented an interplay between individual micro-level (e.g., categorizing resources or temporal dynamics) and collective macro-level (e.g., imitating other users' tags) processes in the form of a novel tag recommender algorithm. The preliminary results for datasets gathered from BibSonomy, CiteULike and Delicious show that our proposed approach can outperform current state-of-the-art algorithms, such as Collaborative Filtering, FolkRank or Pairwise Interaction Tensor Factorization. We conclude that recommender systems can be improved by incorporating related principles of human cognition.