Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations

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.

41 pages, 19 figures, 6 tables, 155 references, TOIS accepted

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Cited by in corpus (17)

• TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
• A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation
• Debiased Contrastive Learning for Sequential Recommendation
• Multi-Behavior Sequential Recommendation with Temporal Graph Transformer
• Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities
• Recommender systems based on graph embedding techniques: A comprehensive review
• Exploiting Positional Information for Session-based Recommendation
• Recommendation Unlearning via Influence Function
• Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation
• Scaling Sequential Recommendation Models with Transformers
• Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation
• RIBBON: Cost-Effective and QoS-Aware Deep Learning Model Inference using a Diverse Pool of Cloud Computing Instances
• Zero-Shot Recommender Systems
• TiM4Rec: An Efficient Sequential Recommendation Model Based on Time-Aware Structured State Space Duality Model
• Pretrained Embeddings for E-commerce Machine Learning: When it Fails and Why?
• Improving Next-Application Prediction with Deep Personalized-Attention Neural Network
• PAS: A Position-Aware Similarity Measurement for Sequential Recommendation