Easy-to-explain feature synthesis approach for recommending entertainment video
The use of dimension reduction techniques has attracted considerable attention owing to information explosion. Without considering the underlying phenomena of interest, traditional dimension reduction approaches aim to search a feature set for optimizing performance. In recommending entertainment videos, beyond the successful recommendations, marketing strategy can be benefited from interpreting precise social context information accurately. Therefore, how to find an easy-to-explain feature set to achieve optimal prediction performance becomes an important issue. In this paper, we propose a three-phase feature synthesis approach to search heuristically optimal feature set within exponential easy-to-explain features. The first phase performs feature selection by screening low-informative features, the second phase shrinks the high-dependent feature subset, and the third phase enhances the dominated features. An implemented social recommendation system and the 11 months purchasing data from the largest commercial entertainment video Web shop in Taiwan are adopted to evaluate the effectiveness and efficiency of the proposed feature synthesis method in the experiments. The experimental results show that our approach can obtain the interpretable clustering results as well as improve the recommendation.
feature synthesis recommendation dimension reduction clustering unsupervised feature selection