Tallrec: An effective and efficient tuning framework to align large language model with recommendation
Large Language Models (LLMs) have demonstrated remarkable performance across
diverse domains, thereby prompting researchers to explore their potential for use in …
diverse domains, thereby prompting researchers to explore their potential for use in …
Multi-task deep recommender systems: A survey
Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual
improvement among tasks considering their shared knowledge. It is an important topic in …
improvement among tasks considering their shared knowledge. It is an important topic in …
Machine learning through the lens of e-commerce initiatives: An up-to-date systematic literature review
E-commerce platforms are a primary place for people to find, compare, and ultimately
purchase products. They employ Machine Learning (ML), Business Intelligence (BI) …
purchase products. They employ Machine Learning (ML), Business Intelligence (BI) …
A bi-step grounding paradigm for large language models in recommendation systems
As the focus on Large Language Models (LLMs) in the field of recommendation intensifies,
the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a …
the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a …
Behavior sequence transformer for e-commerce recommendation in alibaba
Deep learning based methods have been widely used in industrial recommendation
systems (RSs). Previous works adopt an Embedding&MLP paradigm: raw features are …
systems (RSs). Previous works adopt an Embedding&MLP paradigm: raw features are …
Parameter-efficient transfer from sequential behaviors for user modeling and recommendation
Inductive transfer learning has had a big impact on computer vision and NLP domains but
has not been used in the area of recommender systems. Even though there has been a …
has not been used in the area of recommender systems. Even though there has been a …
Entire space multi-task modeling via post-click behavior decomposition for conversion rate prediction
Recommender system, as an essential part of modern e-commerce, consists of two
fundamental modules, namely Click-Through Rate (CTR) and Conversion Rate (CVR) …
fundamental modules, namely Click-Through Rate (CTR) and Conversion Rate (CVR) …
MISSRec: Pre-training and transferring multi-modal interest-aware sequence representation for recommendation
The goal of sequential recommendation (SR) is to predict a user's potential interested items
based on her/his historical interaction sequences. Most existing sequential recommenders …
based on her/his historical interaction sequences. Most existing sequential recommenders …
Transrec: Learning transferable recommendation from mixture-of-modality feedback
As multimedia systems like Tiktok and Youtube become increasingly prevalent, there is a
growing demand for effective recommendation techniques. However, current …
growing demand for effective recommendation techniques. However, current …
Curriculum contrastive context denoising for few-shot conversational dense retrieval
Conversational search is a crucial and promising branch in information retrieval. In this
paper, we reveal that not all historical conversational turns are necessary for understanding …
paper, we reveal that not all historical conversational turns are necessary for understanding …