A survey and illustrative example of hybrid semantic and contextual models for improving diversity and accuracy in personalized tag recommendation

Tag recommendation systems face persistent semantic challenges, including synonymy, polysemy, and contextual ambiguity, which continue to affect recommendation quality despite progress in addressing sparsity and cold start issues. This paper provides a systematic literature review of tag recommendation methods from 2010 to 2025 and introduces a method-challenge mapping framework that links major recommendation paradigms to the specific semantic and structural problems they are best suited to address. Drawing on insights from this review, we include an illustrative hybrid pipeline that demonstrates how CF, CB, and CA techniques can be combined with lightweight natural language processing components such as GloVe-based semantic clustering, sentiment signals, and contextual word sense handling. This example is intended solely as a demonstration of how survey findings can guide practical system design. Using the MovieLens 20 M dataset, we present a small empirical demonstration that shows how integrating semantic and contextual cues can support improvements in precision, recall, and diversity under realistic constraints. The primary contribution of this work is the systematic survey and analytical framework, with the illustrative example serving as a practical companion for researchers and practitioners.