Abstract: This study proposes a novel recommender system that uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user’s preference. The proposed model consists of two steps. In the first step, this study uses logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. Then, this study combines the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. In the second step, this study uses the market basket analysis to extract association rules for co-purchased products. Finally, the system selects customers who have high likelihood to purchase products in each product group and recommends proper products from same or different product groups to them through above two steps. We test the usability of the proposed system by using prototype and real-world transaction and profile data. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The results also show that the proposed system may be useful in real-world online shopping store.
Abstract: Technology changes have been acknowledged as a
critical factor in determining competitiveness of organization. Under
such environment, the right anticipation of technology change has
been of huge importance in strategic planning. To monitor technology
change, technology forecasting (TF) is frequently utilized. In
academic perspective, TF has received great attention for a long time.
However, few researches have been conducted to provide overview of
the TF literature. Even though some studies deals with review of TF
research, they generally focused on type and characteristics of various
TF, so hardly provides information about patterns of TF research and
which TF method is used in certain technology industry. Accordingly,
this study profile developments in and patterns of scholarly research in
TF over time. Also, this study investigates which technology
industries have used certain TF method and identifies their
relationships. This study will help in understanding TF research trend
and their application area.