• Khus Agrawal
• Prof. Prerna Dangra
• Prof. Anupam Chaube

This paper delves into advanced methodologies in recommendation systems, focusing on the pivotal processes of candidate generation and ranking. Through a comprehensive overview, it explores various techniques such as content-based filtering, collaborative filtering, matrix factorization, neural collaborative filtering, self-supervised representation learning, and approximate nearest neighbor search. Each technique is dissected, emphasizing its concept, significance, and practical implementations. Furthermore, the paper discusses the architecture, user profile creation, feature representation, advantages, and challenges of content-based recommendation systems. It also examines collaborative filtering types, matrix factorization challenges, and incremental updates, highlighting Alibaba's Swing Algorithm. Additionally, the integration of neural networks into collaborative filtering, the significance of hyper parameter tuning, and real-world implementations are explored. The concept of self-supervised representation learning, its applications in recommender systems, and notable implementations at Alibaba, Uber, and Instagram are elucidated. Furthermore, the paper elucidates the concept of approximate nearest neighbor search and benchmarks implementations such as Facebook’s FAISS, Google’s ScANN, and hnswlib. The paper also delves into ranking methodologies including logistic regression, shallow neural networks, listwise ranking, and feature crosses, emphasizing their importance and challenges. Evaluation metrics like diversity, coverage, novelty, serendipity, mean reciprocal rank (MRR), and mean average precision (MAP) are discussed. Finally, the paper concludes by summarizing key insights and envisioning future directions in recommendation systems, thus providing a comprehensive understanding of advanced techniques in the field.

Recommendation systems, candidate generation, ranking, content-based filtering, collaborative filtering, matrix factorization, neural collaborative filtering, self-supervised learning, approximate nearest neighbor search, user-based collaborative filtering, item-based collaborative filtering, objective functions, generalized matrix factorization, multi-layer perceptron, self-supervised representation learning, Instagram's ig2vec, Uber's Query2Vec, Alibaba's Random Walks and Skip-Gram Model, Facebook’s FAISS, Google’s ScANN, hnswlib, logistic regression, shallow neural network, listwise ranking, feature crosses, mean reciprocal rank, mean average precision

" ADVANCED TECHNIQUES IN RECOMMENDATION SYSTEMS: AN OVERVIEW OF CANDIDATE GENERATION AND RANKING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.170-181, June-2024, Available :http://www.jetir.org/papers/JETIRGI06028.pdf

" ADVANCED TECHNIQUES IN RECOMMENDATION SYSTEMS: AN OVERVIEW OF CANDIDATE GENERATION AND RANKING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp170-181, June-2024, Available at : http://www.jetir.org/papers/JETIRGI06028.pdf