# Modified Conditional Restricted Boltzmann Machines for Query Recommendation in Digital Archives
## 6 Experiments
### Experiments on ARC-UPD Dataset
This might be because experts are prone to using series names and some subword included in the series names to construct the query (or queries). Using both the artist and series name information could improve from only using the artist information, but this was slightly lower than only using series name information. Adding genre information proved to be a bad influence on all single or combined types of information, which might be because experts rarely use genres to formulate a query (or queries). We further checked some of the representative top 10 query recommendation results of a certain input of each model. The input of all the models was the typical query \"F1\" (meaning \"one hundred aspects of the moon\", a famous book series of ukiyo-e prints that includes stories about moons, which was also used as a series name in the ARC-UPDD). The recommendation queries predicted by each model are shown in Table 2. We regarded all the recommended queries that appeared together with \"F1\" in any metadata fields \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Recall@1** & **Precision@1** & **F1@1** \\ \hline RBMs & 0.685317 & 0.898908 & 0.744208 \\ M-CRBMs (a) & 0.705432 & 0.925713 & 0.766092 \\ M-CRBMs (g) & 0.684667 & 0.900585 & 0.744076 \\ M-CRBMs (s) & **0.723705** & **0.954496** & **0.787330** \\ M-CRBMs (a+g) & 0.687901 & 0.904879 & 0.747577 \\ M-CRBMs (a+s) & 0.715931 & 0.938779 & 0.777305 \\ M-CRBMs (g+s) & 0.663306 & 0.876736 & 0.722109 \\ M-CRBMs (a+g+s) & 0.642967 & 0.852156 & 0.700563 \\ \hline \hline \end{tabular} a = artist, g = genre, s = series name.  
\end{table} 
Table 1: Recall@1, precision@1, and F1@1 of M-CRBM models clamped with different extra information. The results were taken from the final training epoch. The bold text indicates the best results in the column, and the underlined values are the second- and third-best results in the column. Figure 6: Recall@k, precision@k, and F1@k through 25 training epochs of the proposed M-CRBMs that take three types of extra information into account. of the same ukiyo-e as relevant. For example, there is an ukiyo-e print with the series name of \"\(\bar{\bar{\bar{\bar{\
u}}}}\bar{\bar{\bar{\
u}}}\)\" and with the title of \"\(\bar{\bar{\bar{\bar{\
u}}}}\)\" (meaning \"moon of Huaishui River, [PERSON] (who was a Chinese military general and politician of the Wu kingdom)\"). Thus, \"\(\bar{\bar{\bar{\
u}}}\)\" is a relevant query to \"\(\bar{\bar{\bar{\
u}}}\)\". The recommendation results show that, similar to the recall, precision, and F1 score results, M-CRBMs (s) (the M-CRBMs using series names) performed the best. M-CRBMs (s) can give abundant relevant query recommendations. The results also show an interesting finding: the recommendation results were relevant to the metadata field(s) that was (were) used in the M-CRBMs. For example, M-CRBMs that utilize genre information can give some recommendations about the genre (such as \"\(\bar{\bar{\bar{\
u}}}\)\" in M-CRBMs (g), M-CRBMs (a+g), and M-CRBMs (a+g+s)). M-CRBMs that utilize artist information can give some recommendations about the artist (such as \"\(\bar{\bar{\bar{\
u}}}\)\"\(\bar{\bar{\bar{\
u}}}\)\" in M-CRBMs (a)). M-CRBMs that utilize series name information can give some recommendations about the series name (such as \"\(\bar{\bar{\bar{\
u}}}\)\" in M-CRBMs (s), M-CRBMs (a+s), and M-CRBMs (a+g+s)). There existed some noise recommendations, which might have been caused by random sampling in the training and prediction phase, but this could be removed by some processing before developing a system. Two of the typical bad cases of M-CRBMs are listed in Table 3. In the first case, when the input query was \"\(\bar{\bar{\bar{\
u}}}\)\", we could obtain nine relevant output queries. However, many of them were partially duplicated. Such a case exists when there are several partially duplicated queries that are made in the access log. Sometimes it is good to recommend variants of similar queries, but sometimes it may be redundant.