Publication Date
Spring 2025
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Robert Chun
Second Advisor
Navrati Saxena
Third Advisor
Ameya Kadam
Keywords
recommendation systems, collaborative filtering, metadata, cold start, expansion.
Abstract
Recommendation systems power the most popular platforms in the world: from content catalogs on Netflix to custom feeds on TikTok – the importance of recommendation systems is significant. Collaborative filtering, the most popular recommendation technique, is essentially based on the idea of leveraging collective user intelligence i.e., creating recommendations by finding similar users. But this technique suffers when there is not enough data in the profiles of users, formally termed as the cold start problem. This research focuses on this problem by introducing an approach that integrates metadata-driven similarity measures with profile expansion techniques. Our approach combines traditional collaborative filtering with profile expansion and additional signals derived from user and item metadata to enrich sparse user profiles. We use profile expansion strategies to generate pseudo-ratings that when integrated with metadata-based similarities, create a robust hybrid model. Experiments conducted on MovieLens 100k, 1M and MovieDex datasets show that our approach significantly improves performance under severe cold start conditions. This work not only provides a comprehensive framework for enhancing recommendation accuracy in cold start but also outlines key insights for dynamic adjustment of metadata and expansion influence as user profiles & the overall system evolve.
Recommended Citation
Walia, Prabaljit, "Mitigating Cold Start Problem through Metadata Integration and User Preference Analysis" (2025). Master's Projects. 1472.
DOI: https://doi.org/10.31979/etd.cupj-7sku
https://scholarworks.sjsu.edu/etd_projects/1472
