• Corpus ID: 11625138

Efficient Multicore Collaborative Filtering

@article{Wu2011EfficientMC,
  title={Efficient Multicore Collaborative Filtering},
  author={Yao Wu and Qiang Yan and Danny Bickson and Yucheng Low and Qing Yang},
  journal={ArXiv},
  year={2011},
  volume={abs/1108.2580}
}
This paper describes the solution method taken by LeBuSiShu team for track1 in ACM KDD CUP 2011 contest (resulting in the 5th place). We identied two main challenges: the unique item taxonomy characteristics as well as the large data set size. To handle the item taxonomy, we present a novel method called Matrix Factorization Item Taxonomy Regularization (MFITR). MFITR obtained the 2nd best prediction result out of more then ten implemented algorithms. For rapidly computing multiple solutions of… 

Figures and Tables from this paper

Improve collaborative filtering through bordered block diagonal form matrices

This paper presents a novel and general collaborative filtering framework based on (Approximate) Bordered Block Diagonal Form structure of user-item rating matrices, and shows formally that matrices in (A)BBDF structures correspond to community detection on the corresponding bipartite graphs.

Parallel Implementation of the Slope One Algorithm for Collaborative Filtering

Two parallel versions of the collaborative filtering algorithm Slope One are implemented, which has advantages such as its efficiency and the ability to update data dynamically, and its performance is evaluated on a multi-core system.

Project Report: Cs 240a – Applied Parallel Computing K-nearest Neighborhood Based Music Recommendation System

This project has chosen K-Nearest Neighborhood (K-NN) model to predict the ratings for the songs, an item-based algorithm which looks for neighbors among items (songs in this context) unlike user-based algorithms which look for neighborhood among users.

Predicting Search Engine Switching in WSCD 2013 Challenge

This paper describes the solution of GraphLab team that achieves the 4th place for WSCD 2013 Search Engine Switch Detect contest sponsored by Yandex and proposes a two-step ensemble method to blend the authors' individual models in order to fully exploit the dataset and get more accurate result.

Parametric Evaluation Of Collaborative Filtering Over Apache Spark

An implementation over Apache Spark of a typical recommender system is presented here and the evaluation of its prediction correctness, based on the Web movies dataset from Amazon as a benchmark, is in terms of the mean absolute error, the mean square error, and the root meansquare error.

Parallel and Distributed Collaborative Filtering

This work is a survey of parallel and distributed collaborative filtering implementations, aiming to not only provide a comprehensive presentation of the field's development but also offer future research directions by highlighting the issues that need to be developed further.

Cache-conscious graph collaborative filtering on multi-socket multicore systems

This study introduces a dynamic work-stealing mechanism to explore the tradeoff between workload balancing and cache-consciousness and presents a cache- Conscious system for collaborative filtering on modern multi-socket multicore platforms.

Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols

A comprehensive survey and analysis of the state of the art on time-aware recommender systems (TARS), and proposes a methodological description framework aimed to make the evaluation process fair and reproducible.

Subsampling-Based Approximate Monte Carlo for Discrete Distributions

The problem of sampling a discrete random variable with a high degree of dependency that is typical in large-scale Bayesian inference and graphical models is studied, and an efficient approximate solution with a subsampling approach is proposed.

Recommender systems and time context: Characterization of a robust evaluation protocol to increase reliability of measured improvements

Tesis doctoral inedita. Universidad Autonoma de Madrid, Escuela Politecnica Superior, octubre de 2013

References

SHOWING 1-10 OF 15 REFERENCES

Large-Scale Parallel Collaborative Filtering for the Netflix Prize

This paper describes a CF algorithm alternating-least-squares with weighted-?-regularization(ALS-WR), which is implemented on a parallel Matlab platform and shows empirically that the performance of ALS-WR monotonically improves with both the number of features and thenumber of ALS iterations.

Scalable Collaborative Filtering Approaches for Large Recommender Systems

This work proposes various scalable solutions that are validated against the Netflix Prize data set, currently the largest publicly available collection of CF techniques, and proposes various matrix factorization (MF) based techniques.

Factorization meets the neighborhood: a multifaceted collaborative filtering model

The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model and a new evaluation metric is suggested, which highlights the differences among methods, based on their performance at a top-K recommendation task.

One-Class Collaborative Filtering

This paper considers the one-class problem under the CF setting, and proposes two frameworks to tackle OCCF, one based on weighted low rank approximation; the other based on negative example sampling.

The BellKor Solution to the Netflix Grand Prize

Part of the contribution to the “BellKor’s Pragmatic Chaos” final solution, which won the Netflix Grand Prize, is described, which improved the baseline predictors and introduced a new blending algorithm based on gradient boosted decision trees.

Combining predictions for accurate recommender systems

It is shown that linearly combining a set of CF algorithms increases the accuracy and outperforms any single CF algorithm, and how to use ensemble methods for blending predictors in order to outperform a single blending algorithm.

The Yahoo! Music Dataset and KDD-Cup '11

The organizers' account of the KDD-Cup 2011, which challenged the community to identify user tastes in music by leveraging Yahoo! Music user ratings, is provided, including a detailed analysis of the datasets, discussion of the contest goals and actual conduct, and lessons learned throughout the contest.

Combining Predictions for an accurate Recommender System

It is found that simple linear combination of predictions is not optimal in the sense of minimize the prediction RMSE, so a large ensemble of blenders outperforms the neural network as best single blending algorithm.

Collaborative filtering with temporal dynamics

Two leading collaborative filtering recommendation approaches are revamp and a more sensitive approach is required, which can make better distinctions between transient effects and long term patterns.

Recommender systems with social regularization

This paper proposes a matrix factorization framework with social regularization, which can be easily extended to incorporate other contextual information, like social tags, etc, and demonstrates that the approaches outperform other state-of-the-art methods.