Multithreaded Implementation of the Slope One Algorithm for Collaborative Filtering

Recommender systems are mechanisms that filter information and predict a user’s preference to an item. Parallel implementations of recommender systems improve scalability issues and can be applied to internet-based companies having considerable impact on their profits. This paper implements a parallel version of the collaborative filtering algorithm Slope One, which has advantages such as its efficiency and the ability to update data dynamically. The presented version is parallely implemented with the use of the OpenMP API and its performance is evaluated on a multi-core system.

[1]  Nianlong Luo,et al.  One real-time personalized recommendation systems based on Slope One algorithm , 2011, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[2]  Pu Wang,et al.  A Personalized Recommendation Algorithm Combining Slope One Scheme and User Based Collaborative Filtering , 2009, 2009 International Conference on Industrial and Information Systems.

[3]  Abhinav Srivastava,et al.  Distributed Scalable Collaborative Filtering Algorithm , 2011, Euro-Par.

[4]  Muqeet Ali,et al.  Parallel Collaborative Filtering for Streaming Data , 2011 .

[5]  Emmanuel Jeannot,et al.  Euro-Par 2011 Parallel Processing , 2011, Lecture Notes in Computer Science.

[6]  Daniel Lemire,et al.  Slope One Predictors for Online Rating-Based Collaborative Filtering , 2007, SDM.

[7]  Zhi-Dan Zhao,et al.  User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

[8]  Kyungsook Han,et al.  Bio-Inspired Computing and Applications , 2011, Lecture Notes in Computer Science.

[9]  Joseph E. Gonzalez,et al.  GraphLab: A New Parallel Framework for Machine Learning , 2010 .

[10]  DeJia Zhang.  An Item-based Collaborative Filtering Recommendation Algorithm Using Slope One Scheme Smoothing , 2009, 2009 Second International Symposium on Electronic Commerce and Security.

[11]  Zhongfu Wu,et al.  Personalized Context-Aware Collaborative Filtering Based on Neural Network and Slope One , 2009, CDVE.

[12]  Congfu Xu,et al.  A Recommendation Algorithm Combining Clustering Method and Slope One Scheme , 2011, ICIC.

[13]  Zhongfu Wu,et al.  Userrank for item-based collaborative filtering recommendation , 2011, Inf. Process. Lett..

[14]  Qing Yang,et al.  Efficient Multicore Collaborative Filtering , 2011, ArXiv.

[15]  Dennis M. Wilkinson,et al.  Large-Scale Parallel Collaborative Filtering for the Netflix Prize , 2008, AAIM.

[16]  Kyuseok Shim,et al.  Web Technologies and Applications , 2014, Lecture Notes in Computer Science.

[17]  Jiao Wang,et al.  A Slope One Collaborative Filtering Recommendation Algorithm Using Uncertain Neighbors Optimizing , 2011, WAIM Workshops.

[18]  Xing Chen,et al.  Clustering Weighted Slope One for distributed parallel computing , 2011, Proceedings of 2011 International Conference on Computer Science and Network Technology.

[19]  Yongqiang Wang,et al.  Learning to Recommend Based on Slope One Strategy , 2012, APWeb.

[20]  Namita Mittal,et al.  Recommender System Framework Using Clustering and Collaborative Filtering , 2010, 2010 3rd International Conference on Emerging Trends in Engineering and Technology.

[21]  Anupam Joshi,et al.  Highly scalable parallel collaborative filtering algorithm , 2010, 2010 International Conference on High Performance Computing.

[22]  Srujana Merugu,et al.  A scalable collaborative filtering framework based on co-clustering , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[23]  Costas S. Iliopoulos,et al.  A New Efficient Algorithm for Computing the Longest Common Subsequence , 2007, AAIM.

[24]  Joydeep Ghosh,et al.  Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix data , 2009, KDD.

[25]  Peter J. Haas,et al.  Large-scale matrix factorization with distributed stochastic gradient descent , 2011, KDD.

[26]  Jie Lu,et al.  Scaling-Up Item-Based Collaborative Filtering Recommendation Algorithm Based on Hadoop , 2011, 2011 IEEE World Congress on Services.