Empirical Analysis of Session-Based Recommendation Algorithms

Abstract

Recommender systems are tools that support online users by pointing them to potential items of interest in situations of information overload. In recent years, the class of session-based recommendation algorithms received more attention in the research literature. These algorithms base their recommendations solely on the observed interactions with the user in an ongoing session and do not require the existence of long-term preference profiles. Most recently, a number of deep learning based ("neural") approaches to session-based recommendations were proposed. However, previous research indicates that today's complex neural recommendation methods are not always better than comparably simple algorithms in terms of prediction accuracy. With this work, our goal is to shed light on the state-of-the-art in the area of session-based recommendation and on the progress that is made with neural approaches. For this purpose, we compare twelve algorithmic approaches, among them six recent neural methods, under identical conditions on various datasets. We find that the progress in terms of prediction accuracy that is achieved with neural methods is still limited. In most cases, our experiments show that simple heuristic methods based on nearest-neighbors schemes are preferable over conceptually and computationally more complex methods. Observations from a user study furthermore indicate that recommendations based on heuristic methods were also well accepted by the study participants. To support future progress and reproducibility in this area, we publicly share the session-rec evaluation framework that was used in our research.

References in corpus (13)

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Session-based Recommendation with Graph Neural Networks
• Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
• Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms
• Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches
• Evaluation of Session-based Recommendation Algorithms
• Personalized News Recommendation with Context Trees
• Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks
• News Session-Based Recommendations using Deep Neural Networks
• Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation
• Critically Examining the "Neural Hype": Weak Baselines and the Additivity of Effectiveness Gains from Neural Ranking Models
• A Survey on Session-based Recommender Systems
• Context Tree for Adaptive Session-based Recommendation

Cited by in corpus (23)

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• A Survey on Session-based Recommender Systems
• Session-aware Recommendation: A Surprising Quest for the State-of-the-art
• Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities
• Diversification in Session-based News Recommender Systems
• Multi-intent-aware Session-based Recommendation
• A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data
• Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters
• A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services
• Improving Sequential Recommendations with LLMs
• SIGIR 2021 E-Commerce Workshop Data Challenge
• Multi-modal Embedding Fusion-based Recommender
• Learning Recommendations from User Actions in the Item-poor Insurance Domain
• Performance Comparison of Session-based Recommendation Algorithms based on GNNs
• A Worrying Reproducibility Study of Intent-Aware Recommendation Models
• Diffusion Recommender Models and the Illusion of Progress: A Concerning Study of Reproducibility and a Conceptual Mismatch
• Linear Item-Item Model with Neural Knowledge for Session-based Recommendation
• Recommending Target Actions Outside Sessions in the Data-poor Insurance Domain
• eSASRec: Enhancing Transformer-based Recommendations in a Modular Fashion
• Graph-based Recommendation for Sparse and Heterogeneous User Interactions
• Searching Personal Collections