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AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders
Shuai Zhang, Lina Yao, Xiwei Xu
TL;DR
Sparse user-item matrices and hand-crafted hybrid features remain challenges for collaborative filtering. The paper proposes AutoSVD++, which integrates contractive auto-encoder content representations with SVD++ and implicit feedback. Across the evaluated datasets, the proposed models outperform baseline and recent methods, while future work targets richer content and temporal or social information.
Problem
Collaborative filtering struggles with sparse user-item matrices, while many hybrid methods rely on hand-crafted features and insufficiently use implicit feedback.
Method
AutoSVD++ couples contractive auto-encoder item-content representations with SVD++ and incorporates implicit user feedback in a scalable hybrid model.
Results
AutoSVD and AutoSVD++ consistently outperform baseline and recent compared methods overall, with AutoSVD++ outperforming other approaches on two of three datasets.
Takeaways & Limitations
The approach provides an efficient hybrid collaborative-filtering framework that jointly uses compact item-content representations and implicit feedback for item recommendation.
Takeaways & Limitations
The authors identify richer textual and visual content, temporal dynamics, and social-network information as future extensions.
Abstract
from arXiv · showhide
Collaborative filtering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matrix still remains a challenge. To tackle such issue, hybrid CF such as combining with content based filtering and leveraging side information of users and items has been extensively studied to enhance performance. However, most of these approaches depend on hand-crafted feature engineering, which are usually noise-prone and biased by different feature extraction and selection schemes. In this paper, we propose a new hybrid model by generalizing contractive auto-encoder paradigm into matrix factorization framework with good scalability and computational efficiency, which jointly model content information as representations of effectiveness and compactness, and leverage implicit user feedback to make accurate recommendations. Extensive experiments conducted over three large scale real datasets indicate the proposed approach outperforms the compared methods for item recommendation.
1 INTRODUCTION
Collaborative filtering is effective but struggles with sparse user-item matrices, motivating hybrid approaches. AutoSVD++ combines collaborative filtering, contractive auto-encoders, and implicit feedback to improve recommendation performance and efficiency.
- Collaborative filtering learns preferences from user-item interactions but is limited by sparse user-item matrices.
- Hybrid methods address sparsity by combining content-based and collaborative-filtering approaches.
- Many existing hybrid methods rely on hand-crafted feature engineering and inadequately capture nonlinear interactions between content and user-item data.
- AutoSVD++ couples collaborative filtering with a contractive auto-encoder and uses implicit feedback to improve recommendations.
- The proposed model is designed for high scalability and computational efficiency while learning compact semantic representations from item content.
2 PRELIMINARIES
The preliminaries define sparse rating prediction, biased SVD, SVD++, and contractive auto-encoders. Together, these concepts establish how latent factors, implicit feedback, and robust content representations support the paper’s hybrid models.
- 2.1 Problem Definition: The rating matrix contains many missing entries, and the task is to predict ratings for items users have not yet interacted with.
- 2.2.1 Biased SVD: Biased SVD augments matrix factorization with global, user, and item bias terms alongside latent user and item factors.
- 2.2.2 SVD++: SVD++ incorporates implicit information such as browsing or purchasing history to represent user preferences when explicit feedback is limited.
- 2.2.2 SVD++: Implicit feedback is represented through item-associated factor vectors aggregated over items connected to a user.
- 2.3 Contractive Auto-encoders: Contractive auto-encoders learn representations robust to small perturbations by regularizing the Jacobian norm.
- 2.3 Contractive Auto-encoders: A contractive auto-encoder reconstructs high-dimensional input through nonlinear encoder and decoder transformations with learned weights and biases.
3 PROPOSED METHODOLOGY
The paper introduces AutoSVD and AutoSVD++, hybrid models that integrate contractive auto-encoder representations with matrix factorization, with AutoSVD++ additionally incorporating implicit feedback. The models use sequential feature extraction and an efficient training strategy to improve scalability and computational efficiency.
- Model overview: AutoSVD and AutoSVD++ are proposed as two hybrid models combining contractive auto-encoders with collaborative filtering.The models integrate content-derived representations into SVD-based recommendation models.
- AutoSVD: CAE extracts compact, effective, low-dimensional representations from potentially high-dimensional or redundant item side information.The representation cae(C_i) is incorporated into the hybrid model as an item-content component.
- AutoSVD: AutoSVD decomposes each item latent vector into a CAE-derived content representation and a latent item-specific offset, while using a normalization parameter β.User-side information is omitted because user profiles may be incomplete or unavailable due to privacy concerns.
- AutoSVD++: AutoSVD++ extends this design with implicit feedback to address sparse user-item matrices and support recommendation settings centered on implicit interactions.Its prediction incorporates the user latent vector together with an aggregate implicit-feedback factor over items in N(u).
- Optimization: The models use sequential optimization: CAE representations are learned first and then integrated into AutoSVD or AutoSVD++.This avoids recomputing all item content feature vectors when new items arrive, unlike the described simultaneous optimization alternative.
- Optimization: An efficient AutoSVD++ training algorithm reduces the cost of updating implicit-feedback parameters while preserving good performance.The paper states that grouping users who share the same implicit feedback improves computation efficiency.
4 EXPERIMENTS
Experiments evaluate AutoSVD and AutoSVD++ on three public datasets using RMSE, comparing them with baseline and recent models. Results report strong recommendation accuracy and substantially faster training for the efficient AutoSVD++ algorithm.
- Experimental Setup: Experiments evaluate the proposed models on three public datasets, including Movielens-100K, Movielens-1M, and the extremely sparse MovieTweetings dataset.Content features include genres, years, countries, and languages, depending on the dataset.
- Evaluation Metrics: RMSE measures prediction accuracy by comparing predicted ratings with ground-truth ratings in the test set.Each experiment is run five times, and the average RMSE is reported.
- Overall Comparison: AutoSVD and AutoSVD++ consistently outperform the baseline and recent comparison methods across the evaluated datasets.On ML-100K, AutoSVD performs slightly better than AutoSVD++, whereas AutoSVD++ outperforms the other approaches on the other two datasets.
- Scalability: The efficient training algorithm substantially reduces the time complexity of AutoSVD++ compared with traditional SVD++ and the original AutoSVD++.The comparison uses CPU time for one epoch, shown on a log scale.
- Scalability: The optimized AutoSVD++ is reported as R̄ times better than the original AutoSVD++, where R̄ is the average number of items rated by users.The incorporated item content representation and offset do not reduce training efficiency relative to biased SVD.
5 CONCLUSIONS AND FUTURE WORK
The paper presents AutoSVD and AutoSVD++, hybrid collaborative-filtering models that learn item-content representations through contractive auto-encoders, with AutoSVD++ also using implicit feedback. The authors report improved performance and computational efficiency, while identifying richer content, temporal dynamics, and social information as future extensions.
- Conclusions: AutoSVD and AutoSVD++ learn item-content representations through contractive auto-encoders, with AutoSVD++ additionally incorporating implicit feedback.The models are presented as efficient hybrid collaborative-filtering approaches.
- Conclusions: The proposed models are reported to improve recommendation performance and computational efficiency compared with recent methods.The conclusion references experiments on three real-world datasets and faster training for AutoSVD++.
- Future Work: Future work includes richer textual and visual item representations learned through stacked contractive auto-encoders.The authors also propose incorporating temporal dynamics and social-network information.