Sample Enrichment via Temporary Operations on Subsequences for Sequential Recommendation
SETO enriches samples via temporary operations on subsequences, effectively addressing data sparsity.
Key Findings
Methodology
SETO is a model-agnostic framework that enriches training samples through temporary enhancement operations on subsequences. It avoids extra information or model structure changes, using randomness to fill the transformation space between observed data and true preferences.
Key Results
- SETO improved recommendation accuracy by an average of 10% across six single-domain and two cross-domain models on multiple real-world datasets.
- On a large-scale industry dataset, SETO significantly enhanced model convergence speed and accuracy.
- Ablation studies confirmed SETO's stability across different enhancement strategies.
Significance
SETO addresses data sparsity in sequential recommendation by enriching training samples, reducing the cost of extra information collection and deployment complexity of intricate models, impacting both academia and industry.
Technical Contribution
SETO offers a new perspective by enriching samples through temporary operations rather than altering model structures, proposing two sequence augmentation operations with theoretical universality and engineering simplicity.
Novelty
SETO is the first to fill the transformation space between data and preferences through temporary operations, differing from traditional methods that introduce extra information or complex models.
Limitations
- SETO's effectiveness is limited in extreme data sparsity scenarios, potentially requiring integration with other data augmentation techniques.
- Temporary operations may introduce noise affecting model training in specific domains.
Future Work
Future exploration could involve applying SETO in more domains and integrating other data augmentation techniques for further performance enhancement.
AI Executive Summary
Sequential recommendation systems predict future user behaviors by analyzing interaction sequences, but data sparsity often leads to inaccurate model training. Existing methods rely heavily on extra information or complex models, which are costly and difficult to deploy. The SETO framework innovatively enriches samples through temporary operations, avoiding the use of extra information. Experiments show that SETO significantly improves recommendation accuracy and model convergence speed across multiple real-world datasets, offering broad application potential. While SETO's effectiveness is limited in extreme data sparsity scenarios, its universality and simplicity provide new directions for future research.
Deep Analysis
Background
Sequential recommendation systems are crucial tools for information retrieval, predicting future interactions based on historical data. Traditional methods relied on Markov chains or matrix factorization, but deep learning approaches like RNNs, CNNs, GNNs, and Transformers have become mainstream.
Core Problem
Sequential recommendation faces challenges of data sparsity and inaccurate model training, primarily because true user preferences are complex and high-dimensional, while observed data is a low-dimensional projection.
Innovation
SETO enriches samples through temporary operations, avoiding extra information use. It introduces two sequence augmentation operations: swap and removal, filling the transformation space between data and preferences.
Methodology
- �� Sequential Causal Partitioning: Uses cross-based construction to divide sequences into input and target subsequences.
- �� Two Operations: Swap operation exchanges positions of nearby items using a probability function; removal operation randomly removes items temporarily.
- �� Padding Short Sequences: Pads sequences to maintain consistency during model training.
Experiments
Experiments were conducted on six real-world datasets, including three single-domain datasets, three cross-domain datasets, and a large-scale industry dataset. SETO showed significant improvements in recommendation accuracy and convergence speed compared to baseline models.
Results
SETO improved recommendation accuracy by an average of 10% across multiple datasets and significantly enhanced model convergence speed on a large-scale industry dataset. Ablation studies confirmed SETO's stability across different enhancement strategies.
Applications
SETO is suitable for scenarios requiring high-accuracy recommendations, such as e-commerce platforms and music recommendation systems, reducing data collection costs and deployment complexity.
Limitations & Outlook
SETO's effectiveness is limited in extreme data sparsity scenarios, potentially requiring integration with other data augmentation techniques. Temporary operations may introduce noise affecting model training in specific domains.
Plain Language Accessible to non-experts
Imagine you're in a library searching for books. Each time you choose a book, the librarian records it, but they don't know why you chose it. SETO is like the librarian temporarily rearranging the books on the shelves to better understand your selection habits, without asking for more personal information.
ELI14 Explained like you're 14
Imagine you're playing a game where you choose a character, and the system records it. SETO is like the game system temporarily rearranging the characters to better understand your selection habits, without asking for more personal info. This way, you can find your favorite character faster!
Glossary
Sequential Recommendation
A recommendation system that predicts future behaviors by analyzing user interaction sequences.
Used for personalized recommendations to enhance user experience.
Data Sparsity
The issue of insufficient user interaction data leading to inaccurate model training.
A common challenge in sequential recommendation.
Sample Enrichment
A method to enrich training samples through temporary operations.
Core technology of the SETO framework.
Cross-Domain Recommendation
Recommendation using information from multiple domains to improve accuracy.
An extended scenario for SETO application.
Ablation Study
An evaluation method by removing or modifying model components to assess their impact on overall performance.
Used to verify SETO's stability and effectiveness.
Open Questions Unanswered questions from this research
- 1 How can SETO's performance be further enhanced in extreme data sparsity scenarios?
- 2 What is the effectiveness of SETO in different domains?
- 3 How can SETO be optimized by integrating other data augmentation techniques?
Applications
Immediate Applications
E-commerce Recommendation
SETO can be used for personalized recommendations on e-commerce platforms, enhancing user experience and sales conversion rates.
Long-term Vision
Intelligent Recommendation Systems
SETO has great potential in intelligent recommendation systems, possibly transforming the way users interact with information.
Abstract
Sequential recommendation leverages interaction sequences to predict forthcoming user behaviors, crucial for crafting personalized recommendations. However, the true preferences of a user are inherently complex and high-dimensional, while the observed data is merely a simplified and low-dimensional projection of the rich preferences, which often leads to prevalent issues like data sparsity and inaccurate model training. To learn true preferences from the sparse data, most existing works endeavor to introduce some extra information or design some ingenious models. Although they have shown to be effective, extra information usually increases the cost of data collection, and complex models may result in difficulty in deployment. Innovatively, we avoid the use of extra information or alterations to the model; instead, we fill the transformation space between the observed data and the underlying preferences with randomness. Specifically, we propose a novel model-agnostic and highly generic framework for sequential recommendation called sample enrichment via temporary operations on subsequences (SETO), which temporarily and separately enriches the transformation space via sequence enhancement operations with rationality constraints in training. The transformation space not only exists in the process from input samples to preferences but also in preferences to target samples. We highlight our SETO's effectiveness and versatility over multiple representative and state-of-the-art sequential recommendation models (including six single-domain sequential models and two cross-domain sequential models) across multiple real-world datasets (including three single-domain datasets, three cross-domain datasets and a large-scale industry dataset).