Neural Hybrid Ranking Framework Integrating Semantic Search and  Behavioral Signals for Personalized Content Discovery

Saraschandra Arveti ,Anish Hadkar, Mani Teja Nutalapati

Neural Hybrid Ranking Framework Integrating Semantic Search and Behavioral Signals for Personalized Content Discovery

Authors

Saraschandra Arveti ,Anish Hadkar, Mani Teja Nutalapati

Keywords:

Personalized content discovery, semantic search, behavioral signals, neural ranking, recommendation systems

Abstract

The problem of personalized content discovery is far from being resolved as a result ofheterogeneous nature of content along with the growing heterogeneity of user behavior. Conventional approaches typically focus exclusively on content semantics or user interactionpatterns as the means of ranking potentially relevant items

References

Aggarwal, C. C. (2016). Recommender systems (Vol. 1, No. 1). Cham: Springer International Publishing.

Bobadilla, J., Ortega, F., Hernando, A., & Alcalá, J. (2011). Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowledge-based systems, 24(8), 1310-1316.

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Published

2023-12-20

How to Cite

Saraschandra Arveti ,Anish Hadkar, Mani Teja Nutalapati. (2023). Neural Hybrid Ranking Framework Integrating Semantic Search and Behavioral Signals for Personalized Content Discovery . Journal of Computational Analysis and Applications (JoCAAA), 31(4), 2881–2895. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5385