ENHANCING DIVERSITY AND ACCURACY IN PERSONALIZED TAG RECOMMENDATIONS: A HYBRID SEMANTIC AND CONTEXTUAL ANALYSIS APPROACH

Maryam Khanian Najafabadi, Jiayi Chen, Amin Beheshti

Keywords: Personalized content, Tag recommendation, Content-based, Collaborative filtering, Context-aware, Semantic clustering

Abstract:

This paper introduces HYCOMB, a cascading Hybrid model that innovatively integrates
Collaborative Filtering (CF), Content-Based Filtering (CB), and Context-
Aware (CA) methods to address the challenge of data sparsity in tag recommendation
systems. Unlike traditional models that rely heavily on user-item interactions,
HYCOMB enhances recommendation diversity and interpretability by utilizing
semantic clustering in CF to extract and analyze user sentiment from tags, adding
a layer of nuanced understanding often missing in conventional systems. The CB
component advances this by applying sophisticated NLP techniques to refine these
recommendations based on item attributes, while the CA component incorporates
movie synopses for deeper contextual understanding. Developed and tested using
the MovieLens 20M dataset, our model demonstrates significant outperformance
over baseline methods in terms of precision and recall, achieving scores of 0.813
and 0.364 respectively. Further, a newly introduced Overall Total Similarity metric
that underscores its ability to deliver relevant and diverse recommendations.
HYCOMB’s strategic amalgamation of CF, CB, and CA not only mitigates the
effects of sparse data but also improves the precision and diversity of tag recommendations,
reflecting a more accurate alignment with user preferences.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 2722