Y-L Lee, F-H Huang/ Expert Systems with Applications 38(2011)9696-9703 outputs r, the estimation of r, of product p. The process is illus-. 4. Recommendation generation trated in Fig 3. The use of the obtained Fis will be described later. To minimize the amount of user interaction the architecture Recommendation generation is the process that takes account uses Asynchronous JavaScript and XML (AJAX) to elicit the rating of the input variables(i.e. DP, DF, and dG)and produces an estima data via one click from the user. In addition, the architecture uses tion of the output variables (i.e. r). The proposed architecture ha the ANFis module of the Fuzzy Logic Toolbox of MATLAB as the three types of recommendation: information filtering, candidate core of the adaptive behavioral agent representing users' prefer- expansion, and crowd recommendation. ence. The ANFIS used in the proposed architecture is a five-layer The aim of information filtering is to prevent a customer from network with three input variables (i.e. DP, DF, and DG)and one being overwhelmed by the amount of information, such as unfa output variable(estimated r, or r). Fig 4 illustrates the resultant miliar products or a long list of products to choose from. In the pro- FIS of a sample pro-environment and price-sensitive customer. posed architecture, information filtering is very straightforward in Fig 4a is the rating matrix in which rows are (DP, DF, DG, r. Given that the customers' FIs acts like a filter. When a customer c is the characteristics of this customer, the products with low price browsing products in P. the DP, DF, and dg of each product p in and high degree of greenness are given the highest rating(5 Pi is passed to the Fis of the customer FISc and the one with max stars), while the others are given the lowest rating(1 star). The imal r is recommended to the customer, i.e. surface plot of the estimated output and the inputs is shown in VcEC, P=arg max FIS(p, DPp, DFp, DGp) The recommendation can generate one item(maximal r)or a num- ber of items(e.g. top five rs). The process of recommendation gen- eration is illustrated in Fig. 5 Rating data Product database Ad hoc customization Rule p, DP DF DG rI P∈P catena conversion ANFIS FIS of Best or Top-N database/DP, DF, DG) L customer c User model Fig 3. The generation of user model in the pro Fig. 5. The process of the first type of recommendation: information filtering 113311 131313 33 (b) Fig 4.(a) Sample rating data of a pro-environmental and price-sensitive customer; (b)The surface plot of the estimated output and the inputsoutputs r0 , the estimation of r, of product p. The process is illus￾trated in Fig. 3. The use of the obtained FIS will be described later. To minimize the amount of user interaction, the architecture uses Asynchronous JavaScript and XML (AJAX) to elicit the rating data via one click from the user. In addition, the architecture uses the ANFIS module of the Fuzzy Logic Toolbox of MATLAB as the core of the adaptive behavioral agent representing users’ prefer￾ence. The ANFIS used in the proposed architecture is a five-layer network with three input variables (i.e. DP, DF, and DG) and one output variable (estimated r, or r0 ). Fig. 4 illustrates the resultant FIS of a sample pro-environment and price-sensitive customer. Fig. 4a is the rating matrix in which rows are {DP, DF, DG, r}. Given the characteristics of this customer, the products with low price and high degree of greenness are given the highest rating (5 stars), while the others are given the lowest rating (1 star). The surface plot of the estimated output and the inputs is shown in Fig. 4b. 3.4. Recommendation generation Recommendation generation is the process that takes account of the input variables (i.e. DP, DF, and DG) and produces an estima￾tion of the output variables (i.e. r0 ). The proposed architecture has three types of recommendation: information filtering, candidate expansion, and crowd recommendation. The aim of information filtering is to prevent a customer from being overwhelmed by the amount of information, such as unfa￾miliar products or a long list of products to choose from. In the pro￾posed architecture, information filtering is very straightforward in that the customers’ FIS acts like a filter. When a customer c is browsing products in Pi, the DP, DF, and DG of each product p in Pi is passed to the FIS of the customer FISc, and the one with max￾imal r0 is recommended to the customer, i.e. 8c 2 C; p0 ¼ arg max p2Pi FIScðp;DPp;DFp;DGpÞ The recommendation can generate one item (maximal r0 ) or a num￾ber of items (e.g. top five r0 s). The process of recommendation gen￾eration is illustrated in Fig. 5. {p, DP, DF, DG, r | p ∈ Pi } Fig. 3. The generation of user model in the proposed architecture. Fig. 4. (a) Sample rating data of a pro-environmental and price-sensitive customer; (b) The surface plot of the estimated output and the inputs. FIS of customer c User Model Product database Pi Best or Top-N recommendation {DP, DF, DG} ' r Rule adjustment Criteria designation Criteria conversion Ad hoc customization Non-model criteria Fig. 5. The process of the first type of recommendation: information filtering. 9700 Y.-L. Lee, F.-H. Huang / Expert Systems with Applications 38 (2011) 9696–9703