## 3 Research Methodology
### Architecture of the INPPCS  
Through integrating the transfer-learning scheme and CBR, the proposed INPPCS can be built to suggest the new-product portfolios to NPD engineers/managers. Figure 3 presents the proposed architecture of the INPPCS with the incorporation of transfer learning in the case-adaptation process in the CBR cycle. In the proposed system, CBR is applied as the backbone for building new-product portfolios, while transfer learning plays the role of providing attribute weights for similarity measurements through analyzing customer reviews of specific products. To describe the entire architecture, case retrieval and adaptation are two major aspects used to build the new-product portfolios. All new-product ideas in the same category share the same set of product attributes, which can be further applied in the case-based reasoning mechanism. Therefore, a new-product portfolio can be generated by referring to previous successful and similar NPD projects, so as to enhance the products' success and survival rate in the market. Figure 2: General process of fine-tuning BERT. In the case-retrieval process, concrete new-product ideas are consolidated by product development managers/engineers as new-product projects. First of all, to construct an effective project portfolio, historical cases relevant to the new-product projects are retrieved from the case library, where the decision tree using the inductive indexing method is applied to limit the searching space during the case-searching process. In view of this, a two-level decision tree for case indexing is built to retrieve a sub-class of cases, where two stopping conditions are applied: (i) if a project is developing a completely new product (yes or no), and (ii) the target industry of a new product (retail stores or logistics or office use). In addition to the relevance of the retrieved cases, the similarities between the NPD project and all relevant cases are evaluated using Equation (1), where the attribute weights are obtained using a transfer-learning-based approach. Table 1 contains eight attributes that are considered under two attribute classes. However, the weights between the two classes are not assumed to be equal and are determined by using the text classification from the online customer reviews about relevant products. By using the web-crawling method, customer reviews of specific products from the retail e-commerce platforms can be collected as a corpus, where a series of pre-processing steps on textual data are performed, including tokenization, lemmatization, stemming, and stop-words removal. From all the customer reviews, a subset of the reviews was extracted and annotated to fine-tune the bidirectional encoder representations from transformers (BERT) model, while some meaningless and irrelevant comments, such as scam messages, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Class Name** & **Attributes** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{Innovativeness} & \(\bullet\) & Print reliability and resolution (\(X_{1}\), Scale: 1–5); \\  & & \(\bullet\) & Paper cutter reliability (\(X_{2}\), Scale: 1–5); \\  & & \(\bullet\) & Print speed (\(X_{3}\), Unit: mm/s); \\  & & \(\bullet\) & Ease of consumable replacement (\(X_{4}\), Scale: 1–5); \\  & & \(\bullet\) & Port compatibility (\(X_{5}\), Scale: 1–5). \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{Sustainability} & \(\bullet\) & Expected life span (\(X_{6}\), Unit: years); \\  & & \(\bullet\) & Energy consumption (\(X_{7}\), Unit: watt); \\ \cline{1-1}  & & \(\bullet\) & Trade-in value at the end-of-life span (\(X_{8}\), Unit: RMB). \\ \hline \end{tabular}
\end{table} 
Table 1: Classes and attributes for the similarity evaluation. Figure 3: Architecture of the INPPCS. were removed from the dataset. To be specific, the customer reviews are limited to the specific product categories, or even specific brands, and thus customer feedback is highly related to new-product ideas for building portfolios.