Session-based Recommendations with Recurrent Neural Networks (1511.06939v4)

Abstract: We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on short session-based data (e.g. a small sportsware website) instead of long user histories (as in the case of Netflix). In this situation the frequently praised matrix factorization approaches are not accurate. This problem is usually overcome in practice by resorting to item-to-item recommendations, i.e. recommending similar items. We argue that by modeling the whole session, more accurate recommendations can be provided. We therefore propose an RNN-based approach for session-based recommendations. Our approach also considers practical aspects of the task and introduces several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem. Experimental results on two data-sets show marked improvements over widely used approaches.

• The paper introduces a GRU-based RNN model that outperforms traditional recommendation systems in session-based environments.
• It employs session-parallel mini-batches and novel ranking loss functions to effectively predict user interactions.
• Empirical results on RecSys and VIDEO datasets demonstrate substantial gains in Recall@20 and MRR@20 metrics.

Overview of "Session-based Recommendations with Recurrent Neural Networks"

The paper "Session-based Recommendations with Recurrent Neural Networks" by Hidasi, Karatzoglou, Baltrunas, and Tikk addresses a critical issue in the domain of recommender systems: the challenge of making accurate recommendations based on short-term, session-based data rather than long user histories. The authors propose leveraging Recurrent Neural Networks (RNNs), specifically Gated Recurrent Units (GRUs), to model session-based recommendations, which is a departure from the traditional matrix factorization methods that necessitate long user histories to be effective.

Introduction

Recommender systems conventionally employ techniques like matrix factorization and neighborhood methods. However, these approaches falter in session-based environments where user profiles are non-existent or unreliable. The paper argues for the application of RNNs to this specific context, citing the success of these networks in handling sequential data in other domains such as speech recognition and natural language processing. RNNs are well-suited for this task as they can handle short-term dependencies within sessions, predicting the next item a user might interact with based on their current session state.

Related Work

The paper contrasts its approach with several established methods:

• Session-based Recommendation Approaches: Existing methods like item-to-item similarity and Markov Decision Processes (MDPs) are common, but these either fail to account for multiple past interactions within a session or become computationally impractical.
• Deep Learning in Recommenders: Prior work has applied neural networks to collaborative filtering, but this paper is novel in its application of RNNs to session-based recommendations, suggesting several modifications to GRUs to better fit this task.

Methodology

The core suggestion is to use GRUs within RNNs to model the sequential nature of user interactions within a session. Key innovations include:

• Custom GRU-based Architecture: The authors describe using a single-layer GRU due to the typically short session lengths, thus avoiding the complexity of deeper architectures.
• Session-parallel Mini-batches: They introduce a method for creating mini-batches based on sessions rather than in-sequence batches, accounting for variable session lengths and ensuring the independence of sessions.
• Ranking Loss Functions: Given the importance of item ranking in recommender systems, the authors propose a pairwise ranking loss function and a novel TOP1 ranking loss function that outperforms pointwise ranking losses in both stability and effectiveness.

Experimental Evaluation

The authors evaluate their approach on two datasets:

1. RecSys Challenge 2015 Dataset: Consisting of click-stream data from an e-commerce platform.
2. VIDEO Dataset: Collected from a video streaming service, containing watched-video events.

Metrics used for evaluation include Recall@20 and MRR@20, which are critical for practical recommendation systems where only top-ranked items are considered. The GRU-based approach demonstrated significant improvements over popular baselines like Item-KNN and BPR-MF, delivering substantial gains in both recall and MRR across both datasets.

Results

The empirical results showcase the superiority of the proposed GRU-based method:

• RSC15: Achieving a Recall@20 of up to 0.6206 and MRR@20 of up to 0.2693, an improvement over the best baseline, Item-KNN, which had a Recall@20 of 0.5065 and MRR@20 of 0.2048.
• VIDEO: Similarly, achieved up to 0.6624 Recall@20 and 0.3891 MRR@20, outperforming Item-KNN.

The paper underscores the scalability and practicality of the proposed approach, indicating that even large item sets do not significantly compromise performance due to optimized sampling and ranking strategies.

Conclusion and Future Work

The research holds significant implications for the development of more effective recommender systems capable of functioning efficiently in environments where user histories are sparse or unreliable. Future research directions include deeper exploration of network architectures and incorporating content-based item representations to further augment the recommendation accuracy.

The approach detailed in this paper lays a robust foundation for the application of modern RNNs in the recommender systems domain, specifically addressing the nuances of session-based data. This paper exemplifies a meaningful progression from traditional methods to advanced deep learning frameworks in the quest to enhance user experiences in diverse digital ecosystems.