A Content-Based Model for Tag Recommendation in Software Information Sites

Gharibi, Reza; Safdel, Atefeh; Fakhrahmad, Seyed Mostafa; Sadreddini, Mohammad Hadi

doi:10.1093/comjnl/bxz144

Cited by 4 publications

(1 citation statement)

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“…Content-based tag recommendation methods primarily focus on the association between an item's content and its labels, often ignoring user information and behaviors patterns [12][13][14]. Initially, these methods treated the text as a "Bag of Words" (BoW) [15], using word frequency [16] for feature extraction and feeding these features into multi-label classifiers. However, this approach did not accurately capture the importance of each word, leading to the adoption of Term Frequency-Inverse Document Frequency (TF-IDF) [2] for more effective text representation.…”

Section: Content-based Tag Recommendationmentioning

confidence: 99%

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

Huang,

Liu,

Wang

2024

CMC

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In the tag recommendation task on academic platforms, existing methods disregard users' customized preferences in favor of extracting tags based just on the content of the articles. Besides, it uses co-occurrence techniques and tries to combine nodes' textual content for modelling. They still do not, however, directly simulate many interactions in network learning. In order to address these issues, we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations. Specifically, we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network. Then, the Commuting Matrix for massive node pair paths is used to improve computational performance. Finally, the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution. In addition, we also consider solving the model's parameters by applying variational inference. Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-ofthe-art baseline on two real datasets by efficiently merging the three relational data. Based on the Area Under Curve (AUC) and Mean Average Precision (MAP) analysis, the performance of the suggested task is evaluated, and it is demonstrated to have a greater solving efficiency than current techniques.

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