Blockbusters and wallflowers: Accurate, diverse, and scalable recommendations with random walks
F Christoffel, B Paudel, C Newell… - Proceedings of the 9th …, 2015 - dl.acm.org
F Christoffel, B Paudel, C Newell, A Bernstein
Proceedings of the 9th ACM Conference on Recommender Systems, 2015•dl.acm.orgUser satisfaction is often dependent on providing accurate and diverse recommendations. In
this paper, we explore scalable algorithms that exploit random walks as a sampling
technique to obtain diverse recommendations without compromising on accuracy.
Specifically, we present a novel graph vertex ranking recommendation algorithm called RP^
3_beta that re-ranks items based on 3-hop random walk transition probabilities. We show
empirically, that RP^ 3_beta provides accurate recommendations with high long-tail item …
this paper, we explore scalable algorithms that exploit random walks as a sampling
technique to obtain diverse recommendations without compromising on accuracy.
Specifically, we present a novel graph vertex ranking recommendation algorithm called RP^
3_beta that re-ranks items based on 3-hop random walk transition probabilities. We show
empirically, that RP^ 3_beta provides accurate recommendations with high long-tail item …
User satisfaction is often dependent on providing accurate and diverse recommendations. In this paper, we explore scalable algorithms that exploit random walks as a sampling technique to obtain diverse recommendations without compromising on accuracy. Specifically, we present a novel graph vertex ranking recommendation algorithm called RP^3_beta that re-ranks items based on 3-hop random walk transition probabilities. We show empirically, that RP^3_beta provides accurate recommendations with high long-tail item frequency at the top of the recommendation list. We also present scalable approximate versions of RP^3_beta and the two most accurate previously published vertex ranking algorithms based on random walk transition probabilities and show that these approximations converge with increasing number of samples.
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