How to Make Latent Factors Interpretable by Feeding Factorization Machines with Knowledge Graphs

Anelli, Vito Walter; Noia, Tommaso Di; Sciascio, Eugenio Di; Ragone, Azzurra; Trotta, Joseph

doi:10.1007/978-3-030-30793-6_3

Cited by 24 publications

(4 citation statements)

References 45 publications

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“…Built on this strategy, various models were proposed. For instance, by means of a textual knowledge graph, kaHFM [369] can learn item's embeddings of semantic features as a supplement to factorization machine (FM) [153]. After extracting the structural, textual and visual features of items from three different knowledge graphs, CKE [370] can construct a synthetic embedding for each item as a supplement.…”

Section: B Recommendation Involving Knowledgementioning

confidence: 99%

Recommender Systems Based on Graph Embedding Techniques: A Review

Deng

2022

IEEE Access

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As a pivotal tool to alleviate the information overload problem, recommender systems aim to predict user's preferred items from millions of candidates by analyzing observed user-item relations. As for alleviating the sparsity and cold start problems encountered by recommender systems, researchers generally resort to employing side information or knowledge in recommendation as a strategy for uncovering hidden (indirect) user-item relations, aiming to enrich observed information (or data) for recommendation. However, in the face of the high complexity and large scale of side information and knowledge, this strategy largely relies for efficient implementation on the scalability of recommendation models. Not until after the prevalence of machine learning did graph embedding techniques be a recent concentration, which can efficiently utilize complex and large-scale data. In light of that, equipping recommender systems with graph embedding techniques has been widely studied these years, appearing to outperform conventional recommendation implemented directly based on graph topological analysis (or resolution). As the focus, this article systematically retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs and knowledge graphs, and proposes a general design pipeline of that. In addition, after comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models on simulations, this article manifests that the conventional models can still overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the comparative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes constructive suggestions on making a trade-off between graph embedding-based recommendation and conventional recommendation in different tasks, and puts forward some open questions.

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Section: B Recommendation Involving Knowledgementioning

confidence: 99%

Recommender Systems Based on Graph Embedding Techniques: A Review

Deng

2022

IEEE Access

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“…As the study focused on selection and embedding of semantic features, Noia et al showed how ontology-based (linked) data summarization can drive the selection of properties/features useful to a recommender system [18]. Anelli et al showed how to initialize latent factors in Factorization Machines by using semantic features coming from a knowledge graph in order to train an interpretable model [19]. These studies did not directly focus on knowledge expansion or recommendation algorithms fully utilizing knowledge graphs, and knowledge graphs were used as a source of additional information or only some features.…”

Section: Recommendation Algorithmmentioning

confidence: 99%

Learning Knowledge Using Frequent Subgraph Mining from Ontology Graph Data

et al. 2021

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In many areas, vast amounts of information are rapidly accumulating in the form of ontology-based knowledge graphs, and the use of information in these forms of knowledge graphs is becoming increasingly important. This study proposes a novel method for efficiently learning frequent subgraphs (i.e., knowledge) from ontology-based graph data. An ontology-based large-scale graph is decomposed into small unit subgraphs, which are used as the unit to calculate the frequency of the subgraph. The frequent subgraphs are extracted through candidate generation and chunking processes. To verify the usefulness of the extracted frequent subgraphs, the methodology was applied to movie rating prediction. Using the frequent subgraphs as user profiles, the graph similarity between the rating graph and new item graph was calculated to predict the rating. The MovieLens dataset was used for the experiment, and a comparison showed that the proposed method outperformed other widely used recommendation methods. This study is meaningful in that it proposed an efficient method for extracting frequent subgraphs while maintaining semantic information and considering scalability in large-scale graphs. Furthermore, the proposed method can provide results that include semantic information to serve as a logical basis for rating prediction or recommendation, which existing methods are unable to provide.

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“…Based on this strategy, a lot of works were carried out. For instance, kaHFM [368] learns item's embeddings of semantic features from a textual knowledge graph as a supplement to the factorization machine (FM) [153]. By extracting the structure, textual, and visual features of items from three different knowledge graphs, CKE [369] constructs a synthetic embedding for each item as supplementary information.…”

Section: Recommendation Involving Knowledgementioning

confidence: 99%

Recommender systems based on graph embedding techniques: A comprehensive review

Deng

2021

Preprint

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Recommender systems, a pivotal tool to alleviate the information overload problem, aim to predict user's preferred items from millions of candidates by analyzing observed user-item relations. As for tackling the sparsity and cold start problems encountered by recommender systems, uncovering hidden (indirect) user-item relations by employing side information and knowledge to enrich observed information for the recommendation has been proven promising recently; and its performance is largely determined by the scalability of recommendation models in the face of the high complexity and large scale of side information and knowledge. Making great strides towards efficiently utilizing complex and large-scale data, research into graph embedding techniques is a major topic. Equipping recommender systems with graph embedding techniques contributes to outperforming the conventional recommendation implementing directly based on graph topology analysis and has been widely studied these years. This article systematically retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs, and knowledge graphs, and proposes a general design pipeline of that. In addition, comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models, on simulations, manifests that the conventional models overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the relative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes constructive suggestions on making a trade-off between graph embedding-based recommendation and the conventional recommendation in different tasks as well as some open questions.

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How to Make Latent Factors Interpretable by Feeding Factorization Machines with Knowledge Graphs

Cited by 24 publications

References 45 publications

Recommender Systems Based on Graph Embedding Techniques: A Review

Recommender Systems Based on Graph Embedding Techniques: A Review

Learning Knowledge Using Frequent Subgraph Mining from Ontology Graph Data

Recommender systems based on graph embedding techniques: A comprehensive review

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