DeepAI AI Chat
Log In Sign Up

Thematic recommendations on knowledge graphs using multilayer networks

by   Mariano Beguerisse-Díaz, et al.

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.


page 1

page 2

page 3

page 4


Human Curation and Convnets: Powering Item-to-Item Recommendations on Pinterest

This paper presents Pinterest Related Pins, an item-to-item recommendati...

Application of Knowledge Graphs to Provide Side Information for Improved Recommendation Accuracy

Personalized recommendations are popular in these days of Internet drive...

What You Like: Generating Explainable Topical Recommendations for Twitter Using Social Annotations

With over 500 million tweets posted per day, in Twitter, it is difficult...

HybridSVD: When Collaborative Information is Not Enough

We propose a hybrid algorithm for top-n recommendation task that allows ...

Eigenvalue analogy for confidence estimation in item-based recommender systems

Item-item collaborative filtering (CF) models are a well known and studi...

Top-N recommendations in the presence of sparsity: An NCD-based approach

Making recommendations in the presence of sparsity is known to present o...