ACM Transactions on Recommender Systems

In recent years, there has been an increasing recognition that when machine learning (ML) algorithms are used to automate decisions, they may mistreat individuals or groups, with legal, ethical, or economic implications. Recommender systems are prominent ...

Recommender systems are the algorithms which select, filter, and personalize content across many of the world's largest platforms and apps. As such, their positive and negative effects on individuals and on societies have been extensively theorized and ...

Multi-modal interactive recommender systems (MMIRS) can effectively guide users towards their desired items through multi-turn interactions by leveraging the users’ real-time feedback (in the form of natural-language critiques) on previously recommended ...

Embedding representations are a popular approach for modeling users and items in recommender systems, e.g., matrix factorization, two-tower models, or autoencoders, in which items and users are embedded in a low-dimensional, dense embedding space. On the ...

In the recommender systems field, it is increasingly recognized that focusing on accuracy measures is limiting and misguided. Unsurprisingly, in recent years, the field has witnessed more interest in the research of values “beyond accuracy.” This trend is ...

Product bundling represents a prevalent marketing strategy in both offline stores and e-commerce systems. Despite its widespread use, previous studies on bundle recommendation face two significant limitations. Firstly, they rely on noisy datasets, where ...

Simulators can provide valuable insights for researchers and practitioners who wish to improve recommender systems, because they allow one to easily tweak the experimental setup in which recommender systems operate, and as a result lower the cost of ...