Pick & merge: An eficient item filtering scheme for windows store recommendations

Adi Makmal, Liron Allerhand, Jonathan Ephrath, Nir Nice, Hilik Berezin, Noam Koenigstein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


Microsoft Windows is the most popular operating system (OS) for personal computers (PCs). With hundreds of millions of users, its app marketplace, Windows Store, is one of the largest in the world. As such, special considerations are required in order to improve online computational efciency and response times. This paper presents the results of an extensive research of efective fltering method for semi-personalized recommendations. The fltering problem, defned here for the frst time, addresses an aspect that was so far largely overlooked by the recommender systems literature, namely efective and efcient method for removing items from semi-personalized recommendation lists. Semi-personalized recommendation lists serve a common list to a group of people based on their shared interest or background. Unlike fully personalized lists, these lists are cacheable and constitute the majority of recommendation lists in many online stores. This motivates the following question: can we remove (most of) the users' undesired items without collapsing onto fully personalized recommendations? Our solution is based on dividing the users into few subgroups, such that each subgroup receives a diferent variant of the original recommendation list. This approach adheres to the principles of semi-personalization and hence preserves simplicity and cacheability. We formalize the problem of fnding optimal subgroups that minimize the total number of fltering errors, and show that it is combinatorially formidable. Consequently, a greedy algorithm is proposed that flters out most of the undesired items, while bounding the maximal number of errors for each user. Finally, a detailed evaluation of the proposed algorithm is presented using both proprietary and public datasets.

Original languageEnglish
Title of host publicationRecSys 2019 - 13th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Number of pages5
ISBN (Electronic)9781450362436
StatePublished - 10 Sep 2019
Externally publishedYes
Event13th ACM Conference on Recommender Systems, RecSys 2019 - Copenhagen, Denmark
Duration: 16 Sep 201920 Sep 2019

Publication series

NameRecSys 2019 - 13th ACM Conference on Recommender Systems


Conference13th ACM Conference on Recommender Systems, RecSys 2019

Bibliographical note

Publisher Copyright:
© 2019 Association for Computing Machinery.


  • E-commerce
  • Personalization Systems
  • Recommender Systems


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