Pick & merge: an efficient item filtering scheme for Windows store recommendations

Abstract

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 efficiency and response times. This paper presents the results of an extensive research of effective filtering method for semi-personalized recommendations. The filtering problem, defined here for the first time, addresses an aspect that was so far largely overlooked by the recommender systems literature, namely effective and efficient 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 different 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 finding optimal subgroups that minimize the total number of filtering errors, and show that it is combinatorially formidable. Consequently, a greedy algorithm is proposed that filters 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.