Feature-based recommendations for one-to-one marketing

Abstract

Most recommendation systems face challenges from products that change with time, such as popular or seasonal products, since traditional market basket analysis or collaborative filtering analysis are unable to recommend new products to customers due to the fact that the products are not yet purchased by customers. Although the recommendation systems can find customer groups that have similar interests as target customers, brand new products often lack ratings and comments. Similarly, products that are less often purchased, such as furniture and home appliances, have fewer records of ratings; therefore, the chances of being recommended are often lower. This research attempts to analyze customers' purchasing behaviors based on product features from transaction records and product feature databases. Customers' preferences toward particular features of products are analyzed and then rules of customer interest profiles are thus drawn in order to recommend customers products that have potential attraction with customers. The advantage of this research is its ability of recommending to customers brand new products or rarely purchased products as long as they fit customer interest profiles; a deduction which traditional market basket analysis and collaborative filtering methods are unable to do. This research uses a two-stage clustering technique to find customers that have similar interests as target customers and recommend products to fit customers' potential requirements. Customers' interest profiles can explain recommendation results and the interests on particular features of products can be referenced for product development, while a oneto-one marketing strategy can improve profitability for companies. q

Personalized market response analysis for a wide variety of products from sparse transaction data

“…Many statistical and data-mining methods for dimension reduction have been assessed for transaction data analysis: traditional latent class analysis [13], correspondence analysis [14], self-organization maps [38] and joint segmentation [29]. The benefits of such methods are that they can treat a large set of customer and product data to seek hidden patterns in reduced-dimensional space.…”

Market Basket Analysis for Mobile Showroom

“…The K-Apriori algorithm extracts a set of frequent item sets from the data, and then pulls out the rules with the highest information content for different groups of customers by dividing the customers in different cluster. K-Apriori [7] is based on the Apriori property and the Association rule generation procedure of the Apriori algorithm. Initially, the binary data is transformed into real domain using linear Wiener transformation.…”

“…Association rules derived depends on confidence. Frequent item set generation is done using data mining algorithms like Apriori [4], FP-Growth Algorithm [5], Eclat [6] and K-Apriori [7]. Apriori algorithm for frequent item set mining is given below.…”

Improving electronic customers' profile in recommender systems using data mining techniques

“…Recommender systems have recently become popular among many well-known e-businesses such as Amazon.com, CDNow.com, Barnes&Noble.com, and MovieFinder.com. There are different versions of recommender systems based on how it is generated such as content-based filtering (CBF) (Cheung et al, 2003;Weng & Liu, 2004;Cho & Kim, 2004;Hung, 2005;Adomavi-cius & Tuzhilin, 2005;Leung et al, 2006;Shih & Liu, 2008), collaborative filtering (CF) (Karypis, 2001;Sarwar et al, 2001;Cheung et al, 2003;Weng & Liu, 2004;Cho & Kim, 2004;Liua et al, 2005;Adomavicius & Tuzhilin, 2005;Boucher-Ryan & Bridge, 2006;Leung et al, 2006;Shih & Liu, 2008) and hybrid approaches (Burke, 2002;Cho & Kim, 2004;Semeraro et al, 2005;Adomavicius & Tuzhilin, 2005;Choi et al, 2006;Kim et al, 2006;Shih & Liu, 2008;Albadvi and Shahbazi, 2009). The rest of this study is organized as follows.…”