# Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
## 6 Experimental Setup
### Evaluation  
To verify the effectiveness of the proposed model, we adopt six representative and state-of-the-art methods as baselines for performance comparison. Three of them are traditional recommendation methods based on matrix factorization (BPR [38], BPR-HFT [39], and VBPR [40]), and the other three are deep models for product recommendation (DeepCoNN [32], CKE [24], and JRL [31]). * **BPR:** The Bayesian personalized ranking [38] model is a popular method for top-N recommendation that learns latent representations of users and items by optimizing the pairwise preferences between different user-item pairs. In this paper, we adopt matrix factorization as the prediction component for BPR. * **BPR-HFT:** The hidden factors and topics (HFT) model [39] integrates latent factors with topic models for recommendation. The original HFT model is optimized for rating prediction tasks. For fair comparison, we learn the model parameters under the pairwise ranking framework of BPR for top-N recommendation. * **VBPR:** The visual Bayesian personalized ranking [40] model is a state-of-the-art method that incorporate product image features into the framework of BPR for recommendation. * **TransRec:** The translation-based recommendation approach proposed in [25], which takes items as entities and users as relations, and leveraged translation-based embeddings to learn the similarity between user and items for personalized recommendation. We adopted \(L_{2}\) loss function, which was reported to have better performance in [25]. Notice that TransRec is different from our model because our model treats both items and users as entities, and learns embedding representations for different types of knowledge (e.g., brands, categories) as well as their relationships. * **DeepCoNN:** The Deep Cooperative Neural Networks model for recommendation [32] is a neural model that applies a convolutional neural network (CNN) over the textual reviews to jointly model users and items for recommendation. * **CKE:** The collaborative KBE model is a state-of-the-art neural model [24] that integrates text, images, and knowledge base for recommendation. It is similar to our model as they both use text and structured product knowledge, but it builds separate models on each type of data to construct item representations while our model constructs a knowledge graph that jointly embeds all entities and relations. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **CDs \& Vinyl** & **Clothing** & **Cell Phones \&** \\  & & & & **Accessories** & **Beauty** \\ \hline
**Entities** & & & & & \\ \#Reviews & 1,097,591 & 278,677 & 194,439 & 198,502 \\ \#Words per review & \(174.57\pm 177.05\) & \(62.21\pm 60.16\) & \(93.50\pm 131.65\) & \(90.90\pm 91.86\) \\ \#Users & \(75,258\) & 39,387 & 27,879 & 22,363 \\ \#Items & 64,443 & 23,033 & 10,429 & 12,101 \\ \#Brands & 1414 & 1182 & 955 & 2077 \\ \#Categories & 770 & 1,193 & 206 & 248 \\ Density & 0.023\% & 0.031\% & 0.067\% & 0.074\% \\ \hline
**Relations** & & & & \\ \#_Purchase_ per user & \(14.58\pm 39.13\) & \(7.08\pm 3.59\) & \(6.97\pm 4.55\) & \(8.88\pm 8.16\) \\ \#_Mention_ per user & \(2545.92\pm 10,942.31\) & \(440.20\pm 452.38\) & \(652.08\pm 1335.76\) & \(806.89\pm 1344.08\) \\ \#_Mention_ per item & \(2973.19\pm 5490.93\) & \(752.75\pm 909.42\) & \(1743.16\pm 3482.76\) & \(1491.16\pm 2553.93\) \\ \#_Also\_bugfit_ per item & \(57.28\pm 39.22\) & \(61.35\pm 32.99\) & \(56.53\pm 35.82\) & \(73.65\pm 30.69\) \\ \#_Also\_viewed_ per item & \(0.27\pm 1.86\) & \(6.29\pm 6.17\) & \(1.24\pm 4.29\) & \(12.84\pm 8.97\) \\ \#_Bought\_together_ per item & \(0.68\pm 0.80\) & \(0.69\pm 0.90\) & \(0.81\pm 0.77\) & \(0.75\pm 0.72\) \\ \#_Produced\_by_ per item & \(0.21\pm 0.41\) & \(0.17\pm 0.38\) & \(0.52\pm 0.50\) & \(0.83\pm 0.38\) \\ \#_Bdongs\_to_ per item & \(7.25\pm 3.13\) & \(6.72\pm 2.15\) & \(3.49\pm 1.08\) & \(4.11\pm 0.70\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of the 5-core datasets for _CDs & Vinyl_, _Clothing_, _Cell Phones & Accessories_, and _Beauty_ in Amazon.