Thematic recommendations on knowledge graphs using multilayer networks

• URL: http://arxiv.org/abs/2105.05733v1
• Date: Wed, 12 May 2021 15:30:21 GMT
• Title: Thematic recommendations on knowledge graphs using multilayer networks
• Authors: Mariano Beguerisse-D\'iaz, Dimitrios Korkinof, Till Hoffmann
• Abstract summary: We present a framework to generate and evaluate thematic recommendations based on multilayer network representations of knowledge graphs (KGs) In this representation, each layer encodes a different type of relationship in the KG, and directed interlayer couplings connect the same entity in different roles. We apply an adaptation of the personalised PageRank algorithm to multilayer models of KGs to generate item-item recommendations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a framework to generate and evaluate thematic recommendations based on multilayer network representations of knowledge graphs (KGs). In this representation, each layer encodes a different type of relationship in the KG, and directed interlayer couplings connect the same entity in different roles. The relative importance of different types of connections is captured by an intuitive salience matrix that can be estimated from data, tuned to incorporate domain knowledge, address different use cases, or respect business logic. We apply an adaptation of the personalised PageRank algorithm to multilayer models of KGs to generate item-item recommendations. These recommendations reflect the knowledge we hold about the content and are suitable for thematic and/or cold-start recommendation settings. Evaluating thematic recommendations from user data presents unique challenges that we address by developing a method to evaluate recommendations relying on user-item ratings, yet respecting their thematic nature. We also show that the salience matrix can be estimated from user data. We demonstrate the utility of our methods by significantly improving consumption metrics in an AB test where collaborative filtering delivered subpar performance. We also apply our approach to movie recommendation using publicly-available data to ensure the reproducibility of our results. We demonstrate that our approach outperforms existing thematic recommendation methods and is even competitive with collaborative filtering approaches.

Related papers

• Quantifying User Coherence: A Unified Framework for Cross-Domain Recommendation Analysis [69.37718774071793]
  This paper introduces novel information-theoretic measures for understanding recommender systems. We evaluate 7 recommendation algorithms across 9 datasets, revealing the relationships between our measures and standard performance metrics.
  arXiv  Detail & Related papers  (2024-10-03T13:02:07Z)
• Relevance meets Diversity: A User-Centric Framework for Knowledge Exploration through Recommendations [15.143224593682012]
  We propose a novel recommendation strategy that combines relevance and diversity by a copula function. We use diversity as a surrogate of the amount of knowledge obtained by the user while interacting with the system. Our strategy outperforms several state-of-the-art competitors.
  arXiv  Detail & Related papers  (2024-08-07T13:48:24Z)
• How to Surprisingly Consider Recommendations? A Knowledge-Graph-based Approach Relying on Complex Network Metrics [0.2537383030441368]
  We propose a Knowledge Graph based recommender system by encoding user interactions on item catalogs. Our study explores whether network-level metrics on KGs can influence the degree of surprise in recommendations. We experimentally evaluate our approach on two datasets of LastFM listening histories and synthetic Netflix viewing profiles.
  arXiv  Detail & Related papers  (2024-05-14T09:38:44Z)
• Neural Graph Collaborative Filtering Using Variational Inference [19.80976833118502]
  We introduce variational embedding collaborative filtering (GVECF) as a novel framework to incorporate representations learned through a variational graph auto-encoder. Our proposed method achieves up to 13.78% improvement in the recall over the test data.
  arXiv  Detail & Related papers  (2023-11-20T15:01:33Z)
• Graph Neural Bandits [49.85090929163639]
  We propose a framework named Graph Neural Bandits (GNB) to leverage the collaborative nature among users empowered by graph neural networks (GNNs) To refine the recommendation strategy, we utilize separate GNN-based models on estimated user graphs for exploitation and adaptive exploration.
  arXiv  Detail & Related papers  (2023-08-21T15:57:57Z)
• Ordinal Graph Gamma Belief Network for Social Recommender Systems [54.9487910312535]
  We develop a hierarchical Bayesian model termed ordinal graph factor analysis (OGFA), which jointly models user-item and user-user interactions. OGFA not only achieves good recommendation performance, but also extracts interpretable latent factors corresponding to representative user preferences. We extend OGFA to ordinal graph gamma belief network, which is a multi-stochastic-layer deep probabilistic model.
  arXiv  Detail & Related papers  (2022-09-12T09:19:22Z)
• D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning [0.3058685580689604]
  This paper proposes a deep reinforcement learning based recommendation system by utilizing Actor-Critic architecture. The proposed model is able to generate a diverse while relevance recommendation list based on the user's preferences.
  arXiv  Detail & Related papers  (2021-10-28T13:11:29Z)
• DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN [59.160401038969795]
  We propose differentiable sampling on Knowledge Graph for Recommendation with GNN (DSKReG) We devise a differentiable sampling strategy, which enables the selection of relevant items to be jointly optimized with the model training procedure. The experimental results demonstrate that our model outperforms state-of-the-art KG-based recommender systems.
  arXiv  Detail & Related papers  (2021-08-26T16:19:59Z)
• A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation [70.69134448863483]
  Research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models.
  arXiv  Detail & Related papers  (2021-04-27T08:03:52Z)
• Sequential Recommendation with Self-Attentive Multi-Adversarial Network [101.25533520688654]
  We present a Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the effect of context information on sequential recommendation. Our framework is flexible to incorporate multiple kinds of factor information, and is able to trace how each factor contributes to the recommendation decision over time.
  arXiv  Detail & Related papers  (2020-05-21T12:28:59Z)
• Rich-Item Recommendations for Rich-Users: Exploiting Dynamic and Static Side Information [20.176329366180934]
  We study the problem of recommendation system where the users and items to be recommended are rich data structures with multiple entity types. We provide a general formulation for the problem that captures the complexities of modern real-world recommendations. We present two real-world case studies of our formulation and the MEDRES architecture.
  arXiv  Detail & Related papers  (2020-01-28T17:53:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.