# Modified Conditional Restricted Boltzmann Machines for Query Recommendation in Digital Archives
###### Abstract
?\" (meaning \"beauty\", which is one of the most famous themes of ukiyo-e), but experts tend to use more specific queries to limit the result scope, such as \"??? ?\" (meaning \"three beauties\"), \"?? ?\" (meaning \"beauties\" of the present age\") or \"???? ?\" (meaning \"beauty looking back\"). In this example, the queries formulated by experts related to \"?? ?\" are often the whole title or the series name of an ukiyo-e print or the subwords of them. In this article, we utilize the query-item pairs extracted from the access log of the ukiyo-e Portal Database [11] of the Art Research Center (ARC-UPD, Ritsumeikan University) to train the proposed model and conduct our experiments. The ARC-UPD is one of the biggest DAs that stores ukiyo-e prints as digital versions and their extensive metadata (artist, title, genre, etc.). More than 200,000 public ukiyo-e prints can be browsed in the ARC-UPD as of August 2022. The restricted Boltzmann machine (RBM) is a two-layer undirected graphical model with a visible layer of observable variables or features and a hidden layer of latent, representative units. It has been used for many different machine learning tasks, including image generation [12], dimensionality reduction [13], and representation learning of human motion [14]. It was first introduced to the recommendation task by [PERSON] et al. [15]. Compared with RBMs, conditional RBMs (CRBMs) take extra information into account and are often applied to temporal sequences of data, such as the CRBM model proposed by [PERSON], [PERSON] et al. [14] and the CRBM model proposed by [PERSON], [PERSON] et al. [16]. Both RBMs and CRBMs capture the dependencies between visible layer variables by associating energy to each configuration of those variables. Our idea for query recommendation is that similar queries should exist in one configuration. For example, if the queries \"?? ?\" (meaning \"beauty\") and \"???? ?\" (meaning \"three beauties\") are often used to search for one certain ukiyo-e print, then these two queries should be in one configuration. The weight matrices in the energy model extract the dependencies of each of the units (the units can represent queries or metadata values)that finally figure out the configurations. In this case, the RBM model only learns the dependencies among the queries. The CRBM model utilizes extra information to help find the potential relative queries in one configuration. In M-CRBMs, the reduced weight matrix from conventional CRBMs is between the conditional layer and visible layer (i.e., the dependencies directly between the queries and metadata). The details of the methodology will be explained in Sections 3 and 4. The novelty and contributions of this work are as follows:  
1. We propose a method to recommend queries in DAs called modified conditional restricted Boltzmann machines (M-CRBMs). M-CRBMs provide users with different expert knowledge levels to seek information in the database when given an initial query. 2. We modify the conventional CRBMs and construct M-CRBMs by reducing the weight matrices in the model. This makes the model trainable for average-performing computers. Aside from that, we use free energy instead of energy to efficiently train the model. 3. The proposed M-CRBM model is able to predict the queries relevant to the user's query and predict the relevance degree (ranking) simultaneously. This paper is structured as follows. Section 2 states the work related to our research. Section 3 introduces the basic methodology of RBMs and CRBMs. Section 4 states the methodology of our proposed M-CRBMs. Section 5 explains the DA dataset that we use in the experiments. Section 6 describes the experiments, and Section 7 concludes the paper.