Cluster based collaborative filtering with inverted indexing
Author
Subakan, Özlem Nurcan
Advisor
Ulusoy, Özgür
Date
2005Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Show full item recordAbstract
Collectively, a population contains vast amounts of knowledge and modern
communication technologies that increase the ease of communication. However,
it is not feasible for a single person to aggregate the knowledge of thousands
or millions of data and extract useful information from it. Collaborative information
systems are attempts to harness the knowledge of a population and to
present it in a simple, fast and fair manner. Collaborative filtering has been successfully
used in domains where the information content is not easily parse-able
and traditional information filtering techniques are difficult to apply. Collaborative
filtering works over a database of ratings for the items which are rated by
users. The computational complexity of these methods grows linearly with the
number of customers which can reach to several millions in typical commercial
applications. To address the scalability concern, we have developed an efficient
collaborative filtering technique by applying user clustering and using a specific
inverted index structure (so called cluster-skipping inverted index structure) that
is tailored for clustered environments. We show that the predictive accuracy
of the system is comparable with the collaborative filtering algorithms without
clustering, whereas the efficiency is far more improved.
Keywords
Collaborative filteringRecommender systems
Clustering
Inverted files
Performance evaluation