## 4 Experiments  
### Dataset Overview  
We used Amazon Book ([[http://jmcauley.ucsd.edu/data/amazon/](http://jmcauley.ucsd.edu/data/amazon/)]([http://jmcauley.ucsd.edu/data/amazon/](http://jmcauley.ucsd.edu/data/amazon/)), accessed on 1 May 2021) publicly accessible datasets to evaluate the proposed performance of the RHRM framework [62; 63]. The original datasets were collected from May 1996 to July 2014 and contain 8,872,495 reviews from 817,789 users on 562,073 items. Table 2 displays an example of attribution information from the Amazon Book Dataset. Each review contains (1) the ID and name of the reviewer, (2) the ID of the reviewed item, (3) the helpfulness information that including the number of helpful votes and the number of unhelpful votes, (4) rating information, (5) summary reviews and detailed reviews on the item, and (6) reviews published time.  
To conduct experiments effectively, we have built the CNN-BiLSTM hybrid model using the dataset (DS1) collected from May 1996 to December 2011, which contains 2,757,812 reviews from 281,661 users on 223,452 items. In addition, to evaluate the proposed recommendation framework performance, we use the dataset (DS2) collected from January 2012 to July 2014, which contains 6,114,683 reviews from 536,128 users on 338,621 items. The descriptive statistics of the two datasets are summarized in Table 3.  
Among these reviews in DS1, only total voting by at least 10 users as helpful or unhelpful are regarded as a training dataset for helpfulness classification [17; 64]. Following the exiting study's common strategy, we measured helpfulness score as the ratio of helpful votes to the total votes. The distribution of the measured helpfulness score is depicted in Figure 3. To better classify helpful or unhelpful reviews, we preferred only highly helpful reviews (\(\theta_{1}>0.9\)) and unhelpful reviews (\(\theta_{2}<0.2\)) as the training dataset. Figure 4 shows examples of helpful reviews and unhelpful reviews. With this filtered dataset, we train binary models for review helpfulness classification. The DS2 volume is large but highly sparse. Therefore, we filtered the dataset to contain only users with at least 20 interactions [60].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute Name** & **Value** \\ \hline Reviewer ID & A2S166 WSCFIFF5 \\ Item ID & 000100039X \\ Reviewer name & [PERSON] \\ Number of helpful votes & 10 \\ Total number of votes & 25 \\ \multicolumn{2}{c}{I evidently misread the writeup, I thought it was a} \\ Review text & hardback. It was a cheap paperback. I got it as a present \\ \multicolumn{2}{c}{so I couldn’t send it back, but I’m very disappointed for} \\ \multicolumn{2}{c}{the cost!} \\ Rating & 3 \\ Summary headline & Not Bad! \\ Review time & 2012-10-10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: An example Amazon Book dataset review composition.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Period** & **User** & **Item** & **Rating and Reviews** \\ \hline DS1 & May 1996–December 2011 & 281,661 & 223,452 & 2,757,812 \\ DS2 & January 2012–July 2014 & 536,128 & 338,621 & 6,114,683 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptive statistics of the two datasets.