mlsanityData to 7 Oct 2026

Evaluation of Session-based Recommendation Algorithms

arXiv 1803.09587 ↗First posted 26 Mar 2018cs.IR cs.AI cs.LGcited by 37

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Abstract (arXiv)

Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the recommendations according to long-term user profiles. In many real-world applications, however, such long-term profiles often do not exist and recommendations therefore have to be made solely based on the observed behavior of a user during an ongoing session. Given the high practical relevance of the problem, an increased interest in this problem can be observed in recent years, leading to a number of proposals for session-based recommendation algorithms that typically aim to predict the user's immediate next actions. In this work, we present the results of an in-depth performance comparison of a number of such algorithms, using a variety of datasets and evaluation measures. Our comparison includes the most recent approaches based on recurrent neural networks like GRU4REC, factorized Markov model approaches such as FISM or FOSSIL, as well as simpler methods based, e.g., on nearest neighbor schemes. Our experiments reveal that algorithms of this latter class, despite their sometimes almost trivial nature, often perform equally well or significantly better than today's more complex approaches based on deep neural networks. Our results therefore suggest that there is substantial room for improvement regarding the development of more sophisticated session-based recommendation algorithms.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Mar 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 6; 2023: 13; 2024: 3; 2025: 3; 2026: 3.

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  1. 2023Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generationcited by 42
  2. 2023Large Language Models for Intent-Driven Session Recommendationscited by 30
  3. 2023Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendationcited by 14
  4. 2025Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenderscited by 12
  5. 2023APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendationcited by 10
  6. 2025From Clicks to Preference: A Multi-stage Alignment Framework for Generative Query Suggestion in Conversational Systemcited by 9
  7. 2021Beyond NDCG: behavioral testing of recommender systems with RecListcited by 8
  8. 2022SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learningcited by 8
  9. 2021PURS: Personalized Unexpected Recommender System for Improving User Satisfactioncited by 7
  10. 2021Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendationcited by 7
  11. 2022An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detectioncited by 7
  12. 2023A Scalable Framework for Automatic Playlist Continuation on Music Streaming Servicescited by 7
25 more
  1. 2023Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamicscited by 6
  2. 2023RecFusion: A Binomial Diffusion Process for 1D Data for Recommendationcited by 6
  3. 2021Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Samplingcited by 5
  4. 2023Semi-supervised Adversarial Learning for Complementary Item Recommendationcited by 5
  5. 2021Session-aware Linear Item-Item Models for Session-based Recommendationcited by 4
  6. 2023When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?cited by 4
  7. 2023Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Reviewcited by 4
  8. 2022STAR: A Session-Based Time-Aware Recommender Systemcited by 3
  9. 2021Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Reinforcement Learningcited by 2
  10. 2021Metric Learning for Session-based Recommendationscited by 1
  11. 2021Improving Next-Application Prediction with Deep Personalized-Attention Neural Networkcited by 1
  12. 2022An Evaluation Study of Generative Adversarial Networks for Collaborative Filteringcited by 1
  13. 2023Federated Privacy-preserving Collaborative Filtering for On-Device Next App Predictioncited by 1
  14. 2023Performance Comparison of Session-based Recommendation Algorithms based on GNNscited by 1
  15. 2025Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluationcited by 1
  16. 2021Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levelscited by 0
  17. 2022Application of WGAN-GP in recommendation and Questioning the relevance of GAN-based approachescited by 0
  18. 2022A Sequence-Aware Recommendation Method Based on Complex Networkscited by 0
  19. 2023Exploring Popularity Bias in Session-based Recommendationcited by 0
  20. 2024A Simple Yet Effective Approach for Diversified Session-Based Recommendationcited by 0
  21. 2024An Interpretable Alternative to Neural Representation Learning for Rating Prediction -- Transparent Latent Class Modeling of User Reviewscited by 0
  22. 2024Towards Fair and Rigorous Evaluations: Hyperparameter Optimization for Top-N Recommendation Task with Implicit Feedbackcited by 0
  23. 2026SplitLight: An Exploratory Toolkit for Recommender Systems Datasets and Splitscited by 0
  24. 2026Eval4DiRec: A Unified and Systematic Evaluation Framework for Diffusion-based Recommender Systemscited by 0
  25. 2026Do Temporal Link Predictors Need Learned Memory? A Smoothed-Count Baseline with a Handful of Parameterscited by 0