Evaluating recommender systems: survey and framework
E Zangerle, C Bauer - ACM Computing Surveys, 2022 - dl.acm.org
The comprehensive evaluation of the performance of a recommender system is a complex
endeavor: many facets need to be considered in configuring an adequate and effective …
endeavor: many facets need to be considered in configuring an adequate and effective …
A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation
Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …
understanding, research in recommendation has shifted to inventing new recommender …
Promptbench: Towards evaluating the robustness of large language models on adversarial prompts
The increasing reliance on Large Language Models (LLMs) across academia and industry
necessitates a comprehensive understanding of their robustness to prompts. In response to …
necessitates a comprehensive understanding of their robustness to prompts. In response to …
S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization
Recently, significant progress has been made in sequential recommendation with deep
learning. Existing neural sequential recommendation models usually rely on the item …
learning. Existing neural sequential recommendation models usually rely on the item …
Contrastive learning for sequential recommendation
Sequential recommendation methods play a crucial role in modern recommender systems
because of their ability to capture a user's dynamic interest from her/his historical inter …
because of their ability to capture a user's dynamic interest from her/his historical inter …
Llara: Large language-recommendation assistant
Sequential recommendation aims to predict users' next interaction with items based on their
past engagement sequence. Recently, the advent of Large Language Models (LLMs) has …
past engagement sequence. Recently, the advent of Large Language Models (LLMs) has …
BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer
Modeling users' dynamic preferences from their historical behaviors is challenging and
crucial for recommendation systems. Previous methods employ sequential neural networks …
crucial for recommendation systems. Previous methods employ sequential neural networks …
Continuous-time sequential recommendation with temporal graph collaborative transformer
In order to model the evolution of user preference, we should learn user/item embeddings
based on time-ordered item purchasing sequences, which is defined as Sequential …
based on time-ordered item purchasing sequences, which is defined as Sequential …
STAMP: short-term attention/memory priority model for session-based recommendation
Predicting users' actions based on anonymous sessions is a challenging problem in web-
based behavioral modeling research, mainly due to the uncertainty of user behavior and the …
based behavioral modeling research, mainly due to the uncertainty of user behavior and the …
A survey on session-based recommender systems
Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …
consumption, services, and decision-making in the overloaded information era and digitized …