FolkRank++: An Optimization of FolkRank Tag Recommendation Algorithm Integrating User and Item Information

Abstract

The graph-based tag recommendation algorithm FolkRank can effectively utilize the relationships between three entities, namely users, items and tags, and achieve better tag recommendation performance. However, FolkRank does not consider the internal relationships of user-user, item-item and tag-tag. This leads to the failure of FolkRank to effectively map the tagging behavior which contains user neighbors and item neighbors to a tripartite graph. For item-item relationships, we can dig out items that are very similar to the target item, even though the target item may not have a strong connection to these similar items in the user-item-tag graph of FolkRank. Hence this paper proposes an improved FolkRank algorithm named FolkRank++, which fully considers the user-user and item-item internal relationships in tag recommendation by adding the correlation information between users or items. Based on the traditional FolkRank algorithm, an initial weight is also given to target user and target item's neighbors to supply the user-user and item-item relationships. The above work is mainly completed from two aspects: (1) Finding items similar to target item according to the attribute information, and obtaining similar users of the target user according to the history behavior of the user tagging items. (2) Calculating the weighted degree of items and users to evaluate their importance, then assigning initial weights to similar items and users. Experimental results show that this method has better recommendation performance.

Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

“…Techniques such as hierarchical attention models and graph neural networks integrate collaboration signals and generate richer entity representations [24]. Additionally, some works explore different vector spaces to learn entity representations and measure semantic correlations [25], such as using hyperbolic spaces and tangent space optimization [26]. To directly capture the interactions between users, items, and tags, methods like the Tag-aware Attentional Graph Neural Network (TA-GNN) [7] and variational self-encoders [8] have been proposed, which utilize attention mechanisms and metric learning to understand better and leverage these interactions [9].…”

LFGCF: Light Folksonomy Graph Collaborative Filtering for Tag-Aware Recommendation

“…Zhang et al [16,17] incorporated a user-tag-item tripartite graph, which turned out effective for improving the accuracy, diversity, and novelty of recommendations. FolkRank++ was brought up by Zhao et al [18] to dig out the similarities among users and items fully. Focusing on personalized ranking recommendations in a collaborative tagging system, Rendle et al [3] introduced matrix factorization and brought up RTF, which optimized ranking in personalized recommendations.…”