Publications

Abstract : Over the last decade, e-commerce has expanded significantly. An effective recommendation system is needed for better information filtering as more products are available online, particularly clothing and fashion accessories. The literature already includes a number of different recommendations for clothing. However, new problems like computation complexity and data’s exponential growth appear. The recommendation model must be updated frequently because trends change quickly. This paper proposes a collaborative filtering-based recommendation system. The Nearest Neighbor PageRank (NNPR) ranking algorithm, which creates individualized recommendations, combines the PageRank algorithm and the user’s closest neighbors. Unlike the Alternating Least square method, the suggested model is assessed by utilizing the (ALS) algorithm. The experiments use a dataset of Amazon fashion reviews, and the results are recorded in terms of Hit Ratio (HR) and Average Reciprocal Rank (MRR). NNPR has been shown to outperform ALS in active users and cold starters scenarios.