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Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
TL;DR: This survey illustrates the concept of sequential recommendation, proposes a categorization of existing algorithms in terms of three types of behavioral sequence, and summarizes the key factors affecting the performance of DL-based models and conducts corresponding evaluations to demonstrate the effects of these factors.
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Abstract: In the field of sequential recommendation, deep learning (DL)-based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based ones. However, there is little systematic study on DL-based methods, especially regarding to how to design an effective DL model for sequential recommendation. In this view, this survey focuses on DL-based sequential recommender systems by taking the aforementioned issues into consideration. Specifically,we illustrate the concept of sequential recommendation, propose a categorization of existing algorithms in terms of three types of behavioral sequence, summarize the key factors affecting the performance of DL-based models, and conduct corresponding evaluations to demonstrate the effects of these factors. We conclude this survey by systematically outlining future directions and challenges in this field.
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Citations
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A Survey on Session-based Recommender Systems
TL;DR: A systematic and comprehensive review on SBRS is provided and a hierarchical framework is created to categorize the related research issues and methods of SBRS and to reveal its intrinsic challenges and complexities.
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A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation
TL;DR: In this article , the authors conduct a systematic review on neural recommender models, aiming to summarize this field to facilitate researchers and practitioners working on recommender systems by dividing the work into collaborative filtering and information-rich recommendation.
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
TL;DR: TALLRec as mentioned in this paper proposes a fine-tuning framework for aligning large language models with recommendation data, which can significantly enhance the recommendation capabilities of LLMs in the movie and book domains.
Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation
Gabriel de Souza Pereira Moreira,Sara Rabhi,Jeong Min Lee,Ronay Ak,Even Oldridge +4 more
- 13 Sep 2021
TL;DR: Transformer4Rec as discussed by the authors is an open-source library built upon HuggingFace's Transformers library with a similar goal of opening up the advances of NLP based Transformers to the recommender system community and making these advancements immediately accessible for the tasks of sequential and session-based recommendation.
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TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
Keqin Bao,Jizhi Zhang,Yang Zhang,Wenjie Wang,Fuli Feng,Xiangnan He +5 more
- 14 Sep 2023
TL;DR: TALLRec is an effective and efficient tuning framework to align large language models with recommendation tasks. It significantly enhances the recommendation capabilities of LLMs and exhibits robust cross-domain generalization.
104
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