HyperRec: Efficient Recommender Systems with Hyperdimensional Computing

Recommender systems are important tools for many commercial applications such as online shopping websites. There are several issues that make the recommendation task very challenging in practice. The first is that an efficient and compact representation is needed to represent users, items and relations. The second is-sue is that the online markets are changing dynamically, it is thus important that the recommendation algorithm is suitable for fast updates and hardware acceleration. In this paper, we propose a new hardware-friendly recommendation algorithm based on Hyperdimensional Computing, called HyperRec. Unlike existing solutions which leverages floating-point numbers for the data representation, in HyperRec, users and items are modeled with binary vectors in a high dimension. The binary representation enables to perform the reasoning process of the proposed algorithm only using Boolean operations, which is efficient on various computing platforms and suitable for hardware acceleration. In this work, we show how to utilize GPU and FPGA to accelerate the proposed HyperRec. When compared with the state-of-the-art methods for rating prediction, the CPU-based HyperRec implementation is 13.75× faster and consumes 87% less memory, while decreasing the mean squared error (MSE) for the prediction by as much as 31.84%. Our FPGA implementation is on average 67.0× faster and has 6.9× higher energy efficient as compared to CPU. Our GPU implementation further achieves on average 3.1× speedup as compared to FPGA, while providing only 1.2× lower energy efficiency.

[1]  Jan M. Rabaey,et al.  Hyperdimensional computing for noninvasive brain-computer interfaces: Blind and one-shot classification of EEG error-related potentials , 2017 .

[2]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[3]  Tajana Simunic,et al.  F5-HD: Fast Flexible FPGA-based Framework for Refreshing Hyperdimensional Computing , 2019, FPGA.

[4]  Jan M. Rabaey,et al.  Exploring Hyperdimensional Associative Memory , 2017, 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA).

[5]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[6]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[7]  Chao Wang,et al.  An FPGA-Based Accelerator for Neighborhood-Based Collaborative Filtering Recommendation Algorithms , 2015, 2015 IEEE International Conference on Cluster Computing.

[8]  Okko Johannes Räsänen,et al.  Sequence Prediction With Sparse Distributed Hyperdimensional Coding Applied to the Analysis of Mobile Phone Use Patterns , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Neil Yorke-Smith,et al.  A Novel Bayesian Similarity Measure for Recommender Systems , 2013, IJCAI.

[10]  Pentti Kanerva,et al.  Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors , 2009, Cognitive Computation.

[11]  Okko Johannes Räsänen,et al.  Modeling Dependencies in Multiple Parallel Data Streams with Hyperdimensional Computing , 2014, IEEE Signal Processing Letters.

[12]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[13]  Dietmar Jannach,et al.  Are we really making much progress? A worrying analysis of recent neural recommendation approaches , 2019, RecSys.

[14]  Luca Benini,et al.  Hyperdimensional biosignal processing: A case study for EMG-based hand gesture recognition , 2016, 2016 IEEE International Conference on Rebooting Computing (ICRC).

[15]  Jure Leskovec,et al.  Graph Convolutional Neural Networks for Web-Scale Recommender Systems , 2018, KDD.

[16]  Pentti Kanerva.  Computing with 10,000-bit words , 2014, 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[17]  Pentti Kanerva,et al.  Sparse Distributed Memory , 1988 .

[18]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

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

[20]  George Karypis,et al.  A Comprehensive Survey of Neighborhood-based Recommendation Methods , 2011, Recommender Systems Handbook.

[21]  Aditya Joshi,et al.  Language Geometry Using Random Indexing , 2016, QI.

[22]  Heng-Tze Cheng,et al.  Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.

[23]  Tat-Seng Chua,et al.  Neural Collaborative Filtering , 2017, WWW.

[24]  F. Maxwell Harper,et al.  The MovieLens Datasets: History and Context , 2016, TIIS.

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

[26]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[27]  Jure Leskovec,et al.  Hidden factors and hidden topics: understanding rating dimensions with review text , 2013, RecSys.

[28]  Yehuda Koren,et al.  Advances in Collaborative Filtering , 2011, Recommender Systems Handbook.