OCA: ordered clustering-based algorithm for E-commerce recommendation system
The industry of e-commerce (EC) has become more popular and creates tremendous
business opportunities for many firms. Modern societies are gradually shifting towards …
business opportunities for many firms. Modern societies are gradually shifting towards …
Where to go next: Modeling long-and short-term user preferences for point-of-interest recommendation
Abstract Point-of-Interest (POI) recommendation has been a trending research topic as it
generates personalized suggestions on facilities for users from a large number of candidate …
generates personalized suggestions on facilities for users from a large number of candidate …
Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems
Social recommendation leverages social information to solve data sparsity and cold-start
problems in traditional collaborative filtering methods. However, most existing models …
problems in traditional collaborative filtering methods. However, most existing models …
A troubling analysis of reproducibility and progress in recommender systems research
The design of algorithms that generate personalized ranked item lists is a central topic of
research in the field of recommender systems. In the past few years, in particular …
research in the field of recommender systems. In the past few years, in particular …
Jointly learning explainable rules for recommendation with knowledge graph
Explainability and effectiveness are two key aspects for building recommender systems.
Prior efforts mostly focus on incorporating side information to achieve better …
Prior efforts mostly focus on incorporating side information to achieve better …
Adaptive factorization network: Learning adaptive-order feature interactions
Various factorization-based methods have been proposed to leverage second-order, or
higher-order cross features for boosting the performance of predictive models. They …
higher-order cross features for boosting the performance of predictive models. They …
Collaborative filtering with attribution alignment for review-based non-overlapped cross domain recommendation
Cross-Domain Recommendation (CDR) has been popularly studied to utilize different
domain knowledge to solve the data sparsity and cold-start problem in recommender …
domain knowledge to solve the data sparsity and cold-start problem in recommender …
Deep learning and embedding based latent factor model for collaborative recommender systems
A collaborative recommender system based on a latent factor model has achieved
significant success in the field of personalized recommender systems. However, the latent …
significant success in the field of personalized recommender systems. However, the latent …
GAME: Learning graphical and attentive multi-view embeddings for occasional group recommendation
Group recommendation aims to suggest preferred items to a group of users rather than to an
individual user. Most existing methods on group recommendation directly learn theinherent …
individual user. Most existing methods on group recommendation directly learn theinherent …
Quality metrics in recommender systems: Do we calculate metrics consistently?
Offline evaluation is a popular approach to determine the best algorithm in terms of the
chosen quality metric. However, if the chosen metric calculates something unexpected, this …
chosen quality metric. However, if the chosen metric calculates something unexpected, this …