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Optimized Ranking-Based Techniques for Improving Aggregate Recommendation Diversity (Paperback)
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Optimized Ranking-Based Techniques for Improving Aggregate Recommendation Diversity (Paperback)
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Master's Thesis from the year 2013 in the subject Computer Science
- Miscellaneous, grade: 1, course: ME computer science, language:
English, abstract: This paper investigates how demand-side factors
contribute to the Internet's "Long Tail" phenomenon. It first
models how a reduction in search costs will affect the
concentration in product sales. Then, by analyzing data collected
from a multi-channel retailing company, it provides empirical
evidence that the Internet channel exhibits a significantly less
concentrated sales distribution, when compared with traditional
channels. The difference in the sales distribution is highly
significant, even after controlling for consumer differences.
Furthermore, the effect is particularly strong for individuals with
more prior experience using the Internet channel. We find evidence
that Internet purchases made by consumers with prior Internet
experience are more skewed toward obscure products, compared with
consumers who have no such experience. We observe the opposite
outcome when comparing purchases by the same consumers through the
catalog channel. If the relationships we uncover persist, the
underlying trends in technology and search costs portend an ongoing
shift in the distribution of product sales. Singular Value
Decomposition (SVD), together with the Expectation-Maximization
(EM) procedure, can be used to find a low-dimension model that
maximizes the log likelihood of observed ratings in recommendation
systems. However, the computational cost of this approach is a
major concern, since each iteration of the EM algorithm requires a
new SVD computation. We present a novel algorithm that incorporates
SVD approximation into the EM procedure to reduce the overall
computational cost while maintaining accurate predictions.
Furthermore, we propose a new framework for collaborating filtering
in distributed recommendation systems that allows users to maintain
their own rating profiles for privacy. We conduct offline and
online t
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