Item Recommendation From Implicit Feedback

Abstract

The task of item recommendation is to select the best items for a user from a large catalogue of items. Item recommenders are commonly trained from implicit feedback which consists of past actions that are positive only. Core challenges of item recommendation are (1) how to formulate a training objective from implicit feedback and (2) how to efficiently train models over a large item catalogue. This article provides an overview of item recommendation, its unique characteristics and some common approaches. It starts with an introduction to the problem and discusses different training objectives. The main body deals with learning algorithms and presents sampling based algorithms for general recommenders and more efficient algorithms for dot product models. Finally, the application of item recommenders for retrieval tasks is discussed.

Stats

• citations23
• S2 citations—
• github stars0
• HF likes0
• heat score10.35
• arxiv keyrendle2021item

Related papers

• Large-scale Real-time Personalized Similar Product Recommendations (2020)0.00
• Fine-grained Auxiliary Learning For Real-world Product Recommendation (2025)0.00
• Beyond Similarity: Relation Embedding With Dual Attentions For Item-based Recommendation (2019)0.00
• Deep Retrieval: Learning A Retrievable Structure For Large-scale Recommendations (2020)0.00
• Itemrag: Item-based Retrieval-augmented Generation For Llm-based Recommendation (2025)1.20
• Embedding In Recommender Systems: A Survey (2023)0.00
• Divide And Conquer: Towards Better Embedding-based Retrieval For Recommender Systems From A Multi-task Perspective (2023)7.16
• Path-based Deep Network For Candidate Item Matching In Recommenders (2021)7.81