A Hybrid Recommendation System Based on Similar-Price Content in a Large-Scale E-Commerce Environment
Source abstract
Abstract
In large-scale e-commerce, recommendation systems must overcome the shortcomings of conventional models, which often struggle to convert user interest into purchases. This study proposes a revenue-driven recommendation approach that explicitly incorporates user price sensitivity. This study introduces a hybrid recommendation engine that combines collaborative filtering (CF), best match 25 (BM25) for textual relevance, and a price-similarity algorithm. The system is deployed within a scalable three-tier architecture using Elasticsearch and Redis to maintain stability under high-traffic conditions. The system’s performance was evaluated through a large-scale A/B test against both a CF-only model and a popular-item baseline. Results showed that while the CF-only model reduced revenue by 5.10%, our hybrid system increased revenue by 5.55% and improved click-through rate (CTR) by 2.55%. These findings demonstrate that integrating price similarity is an effective strategy for developing commercially viable recommendation systems that enhance both user engagement and revenue growth on large online platforms.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Recommender Systems and Techniques · Image Retrieval and Classification Techniques · Web Data Mining and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department Electrical and Semiconductor Engineering, Chonnam National University, Yeosu 59626, Republic of Korea
Division Electrical and Computing Engineering, Chonnam National University, Yeosu 59626, Republic of Korea
Funding
Ministry of Education
References (43)
Show source-provided references
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