Tem: Tree-enhanced embedding model for explainable recommendation
While collaborative filtering is the dominant technique in personalized recommendation, it
models user-item interactions only and cannot provide concrete reasons for a …
models user-item interactions only and cannot provide concrete reasons for a …
Explainable recommendation through attentive multi-view learning
Recommender systems have been playing an increasingly important role in our daily life
due to the explosive growth of information. Accuracy and explainability are two core aspects …
due to the explosive growth of information. Accuracy and explainability are two core aspects …
Explainable recommendation via interpretable feature mapping and evaluation of explainability
Latent factor collaborative filtering (CF) has been a widely used technique for recommender
system by learning the semantic representations of users and items. Recently, explainable …
system by learning the semantic representations of users and items. Recently, explainable …
Distilling structured knowledge into embeddings for explainable and accurate recommendation
Recently, the embedding-based recommendation models (eg, matrix factorization and deep
models) have been prevalent in both academia and industry due to their effectiveness and …
models) have been prevalent in both academia and industry due to their effectiveness and …
[PDF][PDF] Co-attentive multi-task learning for explainable recommendation.
Despite widespread adoption, recommender systems remain mostly black boxes. Recently,
providing explanations about why items are recommended has attracted increasing …
providing explanations about why items are recommended has attracted increasing …
Disentangling user interest and conformity for recommendation with causal embedding
Recommendation models are usually trained on observational interaction data. However,
observational interaction data could result from users' conformity towards popular items …
observational interaction data could result from users' conformity towards popular items …
An explainable recommendation framework based on an improved knowledge graph attention network with massive volumes of side information
In recent years, explainable recommendation has been a topic of active study. This is
because the branch of the machine learning field related to methodologies is enabling …
because the branch of the machine learning field related to methodologies is enabling …
Personalized prompt learning for explainable recommendation
Providing user-understandable explanations to justify recommendations could help users
better understand the recommended items, increase the system's ease of use, and gain …
better understand the recommended items, increase the system's ease of use, and gain …
Learning heterogeneous knowledge base embeddings for explainable recommendation
Providing model-generated explanations in recommender systems is important to user
experience. State-of-the-art recommendation algorithms—especially the collaborative …
experience. State-of-the-art recommendation algorithms—especially the collaborative …
Multi-modal knowledge-aware reinforcement learning network for explainable recommendation
S Tao, R Qiu, Y Ping, H Ma - Knowledge-Based Systems, 2021 - Elsevier
Abstract Knowledge graphs (KGs) can provide rich, structured information for
recommendation systems as well as increase accuracy and perform explicit reasoning …
recommendation systems as well as increase accuracy and perform explicit reasoning …