This article introduces techniques used in Recommender systems, including association analysis and collaborative filtering, and provides examples of applications that utilize association rules. For more details, please continue reading
1. Recommender system using Association analysis
A recommender system identifies customer needs and recommends products or books to them, or suggests products that customers might need. Examples include Lazada and Amazon, which recommend specific items or books to customers. If a customer likes one product, they are more likely to like another. This approach allows for personalized product recommendations for each individual customer. The technique used is association analysis
Examples of applications that use Association Rules
– Market basket analysis, such as promotions, product placement, inventory management, bundling, product packaging, catalog design, and customer segmentation based on purchasing characteristics
- Managing a website to allow customers to conveniently purchase products and provide product recommendations, such as those from Amazon, Lazada, and Netflix
– Document analysis: Grouping sentences and finding documents with similar keywords to recommend, for example, using Google
– Medical diagnosis, categorizing illnesses, and finding relationships between symptoms and medications used to treat them
– Census data
- Government services include planning in education, healthcare, transportation, and investment
- Private sector: Planning to build new factories, shopping malls, or banks
- Credit card customer relationship management; customer segmentation based on customer behavior
2. Collaborative filtering for recommender system
– User-based nearest-neighbor collaborative filtering recommends products based on similar customer characteristics
– Item-based nearest-neighbor collaborative filtering suggests products of interest based on similar product preferences
– A model-based recommender system on big data involves building a model beforehand to reduce the problem of handling a large number of customer requests and to be able to recommend products that customers are interested in immediately, without having to process each request individually
Speaker: Dr. Pairasan Padungwiang,
R&D BI Co., Ltd.
Compiled by: Nuchanat Rongrong
www.rdbi.co.th
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