Random walks in recommender systems: exact computation and simulations

TY - CHAP

T1 - Random walks in recommender systems: exact computation and simulations

AU - Cooper, Colin

AU - Lee, Sang-Hyuk

AU - Radzik, Tomasz

AU - Siantos, Yiannis

PY - 2014

Y1 - 2014

N2 - A recommender system uses information about known associations between users and items to compute for a given user an ordered recommendation list of items which this user might be interested in acquiring. We consider ordering rules based on various parameters of random walks on the graph representing associations between users and items. We experimentally compare the quality of recommendations and the required computational resources of two approaches: (i) calculate the exact values of the relevant random walk parameters using matrix algebra; (ii) estimate these values by simulating random walks. In our experiments we include methods proposed by Fouss et al. and Gori and Pucci, method P3, which is based on the distribution of the random walk after three steps, and method P3a, which generalises P3. We show that the simple method P3 can outperform previous methods and method P3a can offer further improvements. We show that the time- and memory-efficiency of direct simulation of random walks allows application of these methods to large datasets. We use in our experiments the three MovieLens datasets.

AB - A recommender system uses information about known associations between users and items to compute for a given user an ordered recommendation list of items which this user might be interested in acquiring. We consider ordering rules based on various parameters of random walks on the graph representing associations between users and items. We experimentally compare the quality of recommendations and the required computational resources of two approaches: (i) calculate the exact values of the relevant random walk parameters using matrix algebra; (ii) estimate these values by simulating random walks. In our experiments we include methods proposed by Fouss et al. and Gori and Pucci, method P3, which is based on the distribution of the random walk after three steps, and method P3a, which generalises P3. We show that the simple method P3 can outperform previous methods and method P3a can offer further improvements. We show that the time- and memory-efficiency of direct simulation of random walks allows application of these methods to large datasets. We use in our experiments the three MovieLens datasets.

U2 - 10.1145/2567948.2579244

DO - 10.1145/2567948.2579244

M3 - Conference paper

SN - 978-1-4503-2745-9

SP - 811

EP - 816

BT - 23rd International World Wide Web Conference, WWW '14, Seoul, Republic of Korea, April 7-11, 2014, Companion Volume

A2 - Chung, Chin-Wan

A2 - Broder, Andrei Z.

A2 - Shim, Kyuseok

A2 - Suel, Torsten

PB - ACM New York, NY, USA

ER -