Abstract-In the growing E-commerce market, many online shopping sites have adopted product recommendation systems to expand their business opportunities. We have focused on iGA (interactive Genetic Algorithm) as a solution to product recommendation algorithms. Although iGA is an optimization technique for user's preference through the interaction between systems and users, iGA requires extraction of design variables to apply the product recommendation. Extracting design variables from existing products by hand is unrealistic because there are a wide variety of products on shopping sites that are updated rapidly. To address this problem, we have proposed an automatic generation method of a design variable space based on the collective preference data on the web. In this paper, we introduce several design variable spaces generated by books on Amazon, which maintain recommendation relations among products. The distributions of books generated by the proposed method are influenced by their authors. Then, subjective experiments confirmed that test subjects searched solutions by their unique preference.

Interactive Genetic Algorithm (iGA) is one of evolutionary computations in which the design candidates are evaluated by human. Using iGA, the sensibility and subjective feelings of humans can be optimized by learning the user's evaluation of presented individuals. In this research, iGA was applied to product recommendation on shopping sites. One of the most difficult points to be addressed in construction of a product recommendation system is to taking a long time to extract and assign values to design variables from all of the actual products on the site. It is also difficult to define product design variables appropriately. To address these problems, we propose a method to generate design variables automatically based on a lot of users' preference data on the Web. We constructed the design variables using the relevance of products obtained by Collaborative Filtering and discussed them. Through the simulation experiments, the effectiveness of the proposed method is discussed.

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Previous studies have revealed duplications and diversification of myosin XI genes between angiosperms and bryophytes; however, the functional differentiation and conservation of myosin XI between them remain unclear. Here, we identified a single myosin XI gene from the liverwort Marchantia polymorpha (Mp). The molecular properties of Mp myosin XI are similar to those of Arabidopsis myosin XIs responsible for cytoplasmic streaming, suggesting that the motor function of myosin XI is able to generate cytoplasmic streaming. In cultured Arabidopsis cells, transiently expressed green fluorescent protein (GFP)-fused Mp myosin XI was observed as some intracellular structures moving along the F-actin. These intracellular structures were co-localized with motile endoplasmic reticulum (ER) strands, suggesting that Mp myosin XI binds to the ER and generates intracellular transport in Arabidopsis cells. The tail domain of Mp myosin XI was co-localized with that of Arabidopsis myosin XI-2 and XI-K, suggesting that all these myosin XIs bind to common cargoes. Furthermore, expression of GFP-fused Mp myosin XI rescued the defects of growth, cytoplasmic streaming and actin organization in Arabidopsis multiple myosin XI knockout mutants. The heterologous expression experiments demonstrated the cellular and physiological competence of Mp myosin XI in Arabidopsis. However, the average velocity of organelle transport in Marchantia rhizoids was 0.04 AE 0.01 lm s À1 , which is approximately one-hundredth of that in Arabidopsis cells. Taken together, our results suggest that the molecular properties of myosin XI are conserved, but myosin XI-driven intracellular transport in vivo would be differentiated from bryophytes to angiosperms.