2024-12-18
arXiv

SAFERec: Self-Attention and Frequency Enriched Model for Next Basket Recommendation

Oleg Lashinin , Denis Krasilnikov , Aleksandr Milogradskii , Marina Ananyeva
SAFERec, a new algorithm for Next-Basket Recommendation, enhances transformer-based models by incorporating item frequency information, improving their performance on NBR tasks. Experiments show SAFERec outperforms other baselines, with an 8% improvement in Recall@10.
Transformer-based approaches such as BERT4Rec and SASRec demonstrate strong performance in Next Item Recommendation (NIR) tasks. However, applying these architectures to Next-Basket Recommendation (NBR) tasks, which often involve highly repetitive interactions, is challenging due to the vast number of possible item combinations in a basket. Moreover, frequency-based methods such as TIFU-KNN and UP-CF still demonstrate strong performance in NBR tasks, frequently outperforming deep-learning approaches. This paper introduces SAFERec, a novel algorithm for NBR that enhances transformer-based architectures from NIR by incorporating item frequency information, consequently improving their applicability to NBR tasks. Extensive experiments on multiple datasets show that SAFERec outperforms all other baselines, specifically achieving an 8\% improvement in Recall@10.
2024-04-25
arXiv

A Survey of Generative Search and Recommendation in the Era of Large Language Models

Yongqi Li , Xinyu Lin , Wenjie Wang , Fuli Feng , Liang Pang
The paper surveys the emerging paradigm of generative search and recommendation driven by large language models, providing a unified framework to categorize and analyze existing works. It highlights unique challenges, open problems, and future directions in this field.
With the information explosion on the Web, search and recommendation are foundational infrastructures to satisfying users' information needs. As the two sides of the same coin, both revolve around the same core research problem, matching queries with documents or users with items. In the recent few decades, search and recommendation have experienced synchronous technological paradigm shifts, including machine learning-based and deep learning-based paradigms. Recently, the superintelligent generative large language models have sparked a new paradigm in search and recommendation, i.e., generative search (retrieval) and recommendation, which aims to address the matching problem in a generative manner. In this paper, we provide a comprehensive survey of the emerging paradigm in information systems and summarize the developments in generative search and recommendation from a unified perspective. Rather than simply categorizing existing works, we abstract a unified framework for the generative paradigm and break down the existing works into different stages within this framework to highlight the strengths and weaknesses. And then, we distinguish generative search and recommendation with their unique challenges, identify open problems and future directions, and envision the next information-seeking paradigm.