Self-supervised learning for recommender systems: A survey
In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …
tremendous success, but they still fall short of expectation when dealing with highly sparse …
Transformers4rec: Bridging the gap between nlp and sequential/session-based recommendation
Much of the recent progress in sequential and session-based recommendation has been
driven by improvements in model architecture and pretraining techniques originating in the …
driven by improvements in model architecture and pretraining techniques originating in the …
Applications of fusion techniques in e-commerce environments: A literature review
E Daskalakis, K Remoundou, N Peppes, T Alexakis… - Sensors, 2022 - mdpi.com
The extreme rise of the Internet of Things and the increasing access of people to web
applications have led to the expanding use of diverse e-commerce solutions, which was …
applications have led to the expanding use of diverse e-commerce solutions, which was …
M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations
Session-based recommender systems (SBRSs) have shown superior performance over
conventional methods. However, they show limited scalability on large-scale industrial …
conventional methods. However, they show limited scalability on large-scale industrial …
Beyond co-occurrence: Multi-modal session-based recommendation
Session-based recommendation is devoted to characterizing preferences of anonymous
users based on short sessions. Existing methods mostly focus on mining limited item co …
users based on short sessions. Existing methods mostly focus on mining limited item co …
Beyond ndcg: behavioral testing of recommender systems with reclist
As with most Machine Learning systems, recommender systems are typically evaluated
through performance metrics computed over held-out data points. However, real-world …
through performance metrics computed over held-out data points. However, real-world …
From abstract to details: A generative multimodal fusion framework for recommendation
In E-commerce recommendation, Click-Through Rate (CTR) prediction has been extensively
studied in both academia and industry to enhance user experience and platform benefits. At …
studied in both academia and industry to enhance user experience and platform benefits. At …
GPU accelerated boosted trees and deep neural networks for better recommender systems
C Deotte, B Liu, B Schifferer, G Titericz - Proceedings of the …, 2021 - dl.acm.org
In this paper we present our 1st place solution of the ACM RecSys 2021 challenge. Twitter
provided a dataset of around 1 billion tweets-user pairs to develop models predicting user …
provided a dataset of around 1 billion tweets-user pairs to develop models predicting user …
Context-aware and Click Session-based Graph Pattern Mining with Recommendations for Smart EMS through AI
In the field of Artificial Intelligence (AI), Smart Enterprise Management Systems (Smart EMS)
and big data analytics are the most prominent computing technologies. A key component of …
and big data analytics are the most prominent computing technologies. A key component of …
Time-dependent next-basket recommendations
S Naumov, M Ananyeva, O Lashinin… - … on Information Retrieval, 2023 - Springer
There are various real-world applications for next-basket recommender systems. One of
them is guiding a website user who wants to buy anything toward a collection of items …
them is guiding a website user who wants to buy anything toward a collection of items …