TY - GEN

T1 - Recommender systems with non-binary grades

AU - Azar, Yossi

AU - Nisgav, Aviv

AU - Patt-Shamir, Boaz

PY - 2011

Y1 - 2011

N2 - We consider the interactive model of recommender systems, in which users are asked about just a few of their preferences, and in return the system outputs an approximation of all their preferences. The measure of performance is the probe complexity of the algorithm, defined to be the maximal number of answers any user should provide (probe complexity typically depends inversely on the number of users with similar preferences and on the quality of the desired approximation). Previous interactive recommendation algorithms assume that user preferences are binary, meaning that each object is either "liked" or "disliked" by each user. In this paper we consider the general case in which users may have a more refined scale of preference, namely more than two possible grades. We show how to reduce the non-binary case to the binary one, proving the following results. For discrete grades with s possible values, we give a simple deterministic reduction that preserves the approximation properties of the binary algorithm at the cost of increasing probe complexity by factor s. Our main result is for the general case, where we assume that user grades are arbitrary real numbers. For this case we present an algorithm that preserves the approximation properties of the binary algorithm while incurring only polylogarithmic overhead.

AB - We consider the interactive model of recommender systems, in which users are asked about just a few of their preferences, and in return the system outputs an approximation of all their preferences. The measure of performance is the probe complexity of the algorithm, defined to be the maximal number of answers any user should provide (probe complexity typically depends inversely on the number of users with similar preferences and on the quality of the desired approximation). Previous interactive recommendation algorithms assume that user preferences are binary, meaning that each object is either "liked" or "disliked" by each user. In this paper we consider the general case in which users may have a more refined scale of preference, namely more than two possible grades. We show how to reduce the non-binary case to the binary one, proving the following results. For discrete grades with s possible values, we give a simple deterministic reduction that preserves the approximation properties of the binary algorithm at the cost of increasing probe complexity by factor s. Our main result is for the general case, where we assume that user grades are arbitrary real numbers. For this case we present an algorithm that preserves the approximation properties of the binary algorithm while incurring only polylogarithmic overhead.

KW - collaborative filtering

KW - recommendation systemes

UR - http://www.scopus.com/inward/record.url?scp=79959674448&partnerID=8YFLogxK

U2 - 10.1145/1989493.1989528

DO - 10.1145/1989493.1989528

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AN - SCOPUS:79959674448

SN - 9781450307437

T3 - Annual ACM Symposium on Parallelism in Algorithms and Architectures

SP - 245

EP - 252

BT - SPAA'11 - Proceedings of the 23rd Annual Symposium on Parallelism in Algorithms and Architectures

T2 - 23rd ACM Symposium on Parallelism in Algorithms and Architectures, SPAA'11

Y2 - 4 June 2011 through 6 June 2011

ER -