Hybrid Cosmetics Recommender System
Date Issued
2008
Date
2008
Author(s)
Huang, Chih-Cheng
Abstract
With the recent rise of Web 2.0 concepts and the advent of a long tail economy, more and more content can be obtained though the Web. Consumers now have much more alternatives than ever before. Nonetheless, the plenty of choices is itself a blessing and a curse. las, the recent birth of the recommender system, which aims to find the items that a specific user might be interested in, provides us with a new remedy. So much effort has been devoted to this area of research and four different approaches; namely, collective filtering, content filtering, knowledge-based, and demographic; have become the four major recommendation techniques. Each has its own pros and cons. As a result, one of the branches of recommender system research is to blend these mechanisms into a single hybrid. n this paper, we extrapolate the feasibility of the feature combination hybrid method by merging the collective filtering and demographic techniques. Meanwhile, an idea from data mining field was borrowed to develop a new way in computing the similarity between users. We also combine the content filtering and knowledge-based by using the feature augmentation hybrid method to filter out similar products. Skin care products are chosen to be our proof-of-concepts due to their often semi-standard product nature, their general high price, and the high user involvement in the purchasing process. he empirical result demonstrates that our approach has similar prediction accuracy as the Pearson correlation metric, proven to be the most accurate one in terms of mean absolute error, while at the same time having higher classification and ranking accuracy. The participants also reveal having satisfactory level of system usefulness, novelty, adoption and satisfaction. It is therefore our strong believe that our contribution lies in the building of a novel and improved approach for recommending goods and services.
Subjects
hybrid recommender system
cosmetics recommendation
feature augmentation
feature combination
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