NDLI: Scalable Collaborative Filtering Recommendation Algorithm with MapReduce

Content Provider	IEEE Xplore Digital Library
Author	Yang Shang Zhiyang Li Wenyu Qu Yujie Xu Zining Song Xuefei Zhou
Copyright Year	2014
Description	Author affiliation: Coll. of Inf. Sci. & Technol., Dalian Maritime Univ., Dalian, China (Yang Shang; Zhiyang Li; Wenyu Qu; Yujie Xu; Zining Song; Xuefei Zhou)
Abstract	Collaborative Filtering (CF) algorithm is the common solution to Recommender System (RS). With the development of network and storage technology, the amount of users and items in RS system is exclusively growing. How to increase the scalability and recommendation accuracy of CF are the main concerns in the related research. In this paper, an efficient implementation for user-based CF algorithm on MapReduce is presented. We exploit Bag of Word (BoW) method and design a hierarchical inverted index to further increase the scalability of our method. Meanwhile, a soft-assignment mechanism for the hierarchical inverted index is proposed to make up the recommendation accuracy decrease caused by the index. The Mapreduce implementations of our methods are detailed discussed and analyzed on both simulated data and real data, demonstrating that our implementation has the ability to scale to huge numbers of users and items, eanwhile ensures recommendation accuracy.
Sponsorship	IEEE Comput. Soc.
Starting Page	103
Ending Page	108
File Size	615932
Page Count	6
File Format	PDF
e-ISBN	9781479950799
DOI	10.1109/DASC.2014.27
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Accuracy Collaborative Filtering Scalability Query processing inverted index Buildings Partitioning algorithms Indexes MapReduce
Content Type	Text
Resource Type	Article

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