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 David Ben-Shimon

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Average citations per article3.38
Citation Count27
Publication count8
Publication years2013-2016
Available for download6
Average downloads per article267.83
Downloads (cumulative)1,607
Downloads (12 Months)333
Downloads (6 Weeks)22
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8 results found Export Results: bibtexendnoteacmrefcsv

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1
December 2016 Expert Systems with Applications: An International Journal: Volume 64 Issue C, December 2016
Publisher: Pergamon Press, Inc.
Bibliometrics:
Citation Count: 0

SVD suffers from computational limitation when delivering top-N items online.An ensemble algorithm for getting top-N items from the SVD results is proposed.The algorithm maps the items to the leaves of multiple compact trees offline.Users are assigned online to one leaf in each tree for obtaining their top-N items.The algorithm delivers ...
Keywords: Matrix factorization, Singular value decomposition, Top-N recommendations, Ensemble methods, Recommender systems

2 published by ACM
February 2016 ACM Transactions on Intelligent Systems and Technology (TIST) - Regular Papers, Survey Papers and Special Issue on Recommender System Benchmarks: Volume 7 Issue 3, April 2016
Publisher: ACM
Bibliometrics:
Citation Count: 3
Downloads (6 Weeks): 4,   Downloads (12 Months): 73,   Downloads (Overall): 256

Full text available: PDFPDF
Recommender systems (RS) can now be found in many commercial Web sites, often presenting customers with a short list of additional products that they might purchase. Many commercial sites do not typically have the ability and resources to develop their own system and may outsource the RS to a third ...
Keywords: Recommender systems, anytime algorithms, collaborative filtering

3 published by ACM
September 2015 RecSys '15 Challenge: Proceedings of the 2015 International ACM Recommender Systems Challenge
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 5,   Downloads (12 Months): 62,   Downloads (Overall): 234

Full text available: PDFPDF
RecSys Challenge 2015 is about predicting the items a user will buy in a given click session. We describe the in-house solution to the challenge as guided by the YOOCHOOSE team. The presented solution achieved 14th place in the challenge's final leaderboard with a score of 51,932 points, while the ...
Keywords: In-House Solution, RecSys Challenge 2015, Recommender systems

4 published by ACM
September 2015 RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems
Publisher: ACM
Bibliometrics:
Citation Count: 19
Downloads (6 Weeks): 8,   Downloads (12 Months): 153,   Downloads (Overall): 502

Full text available: PDFPDF
The 2015 ACM Recommender Systems Challenge offered the opportunity to work on a large-scale e-commerce dataset from a big retailer in Europe which is accepting recommender system as a service from YOOCHOOSE. Participants tackled the problem of predicting what items a user intends to purchase, if any, given a click ...
Keywords: e-commerce, recommender systems, recsys challenge 2015, yoochoose

5
January 2015 ICAART 2015: Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2
Publisher: SCITEPRESS - Science and Technology Publications, Lda
Bibliometrics:
Citation Count: 0

Item-based Collaborative Filtering (CF) models offer good recommendations with low latency. Still, constructing such models is often slow, requiring the comparison of all item pairs, and then caching for each item the list of most similar items. In this paper we suggest methods for reducing the number of item pairs ...
Keywords: Locality Sensitive Hashing, Collaborative-Filtering, top-N recommendations, Item-Based

6 published by ACM
October 2014 RecSys '14: Proceedings of the 8th ACM Conference on Recommender systems
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 2,   Downloads (12 Months): 15,   Downloads (Overall): 260

Full text available: PDFPDF
Many small and medium e-commerce retailers and publishers use recommender systems (RS) to personalize the website content. Many of them do not have an on premise solution for doing that, but rather contact a company that delivers the RS as a service to their website. The service is then responsible ...
Keywords: management, recommender system as a service, graphic user interface, configuration

7 published by ACM
February 2014 IUI Companion '14: Proceedings of the companion publication of the 19th international conference on Intelligent User Interfaces
Publisher: ACM
Bibliometrics:
Citation Count: 4
Downloads (6 Weeks): 1,   Downloads (12 Months): 12,   Downloads (Overall): 118

Full text available: PDFPDF
Many small and mid-sized e-businesses wish to integrate a recommender system into their website. Integrating an existing recommender system to a website often requires certain expertise and programming efforts, thus incurs substantial investments and may not be justified by the added value of the recommender system. This demo presents a ...
Keywords: integration, collaborative filtering, recommender system as a service

8 published by ACM
October 2013 RecSys '13: Proceedings of the 7th ACM conference on Recommender systems
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 2,   Downloads (12 Months): 18,   Downloads (Overall): 237

Full text available: PDFPDF
Many small and mid-sized e-businesses use the services of recommender system (RS) provider companies to outsource the construction and maintenance of their RS. The fees that RS providers charge their clients must cover the computation costs for constructing and updating the recommendation model. By using anytime algorithms, a RS provider ...
Keywords: locality sensitive hashing, item-based, anytime algorithms, collaborative filtering



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