DOTNET PROJECT
Clustering-Based
Collaborative Filtering Using an Incentivized/Penalized User Model
Abstract:
Giving or recommending
appropriate content based on the quality of experience is the most important
and challenging issue in recommender systems. As collaborative filtering (CF)
is one of the most prominent and popular techniques used for recommender systems,
we propose a new clusteringbased CF (CBCF) method using an
incentivized/penalized user (IPU) model only with the ratings given by users,
which is thus easy to implement. We aim to design such a simple
clustering-based approach with no further prior information while improving the
recommendation accuracy. To be precise, the purpose of CBCF with the IPU model
is to improve recommendation performance such as precision, recall, and F1
score by carefully exploiting different preferences among users. Specifically,
we formulate a constrained optimization problem in which we aim to maximize the
recall (or equivalently F1 score) for a given precision. To this end, users are
divided into several clusters based on the actual rating data and Pearson
correlation coefficient. Afterward, we give each item an incentive/penalty
according to the preference tendency by users within the same cluster. Our
experimental results show a significant performance improvement over the
baseline CF scheme without clustering in terms of recall or F1 score for a
given precision.
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