Cluster searching strategies for collaborative recommendation systems

In-memory nearest neighbor computation is a typical collaborative filtering approach for high recommendation accuracy. However, this approach is not scalable given the huge number of customers and items in typical commercial applications. Cluster-based collaborative filtering techniques can be a remedy for the efficiency problem, but they usually provide relatively lower accuracy figures, since they may become over-generalized and produce less-personalized recommendations. Our research explores an individualistic strategy which initially clusters the users and then exploits the members within clusters, but not just the cluster representatives, during the recommendation generation stage. We provide an efficient implementation of this strategy by adapting a specifically tailored cluster-skipping inverted index structure. Experimental results reveal that the individualistic strategy with the cluster-skipping index is a good compromise that yields high accuracy and reasonable scalability figures.


Exploiting Index Pruning Methods for Clustering XML Collections
Altıngövde, İsmail Sengör; Ulusoy, Ozgur (2010-01-01)
In this paper, we first employ the well known Cover-Coefficient Based Clustering Methodology (C3 M) for clustering XML documents. Next, we apply index pruning techniques from the literature to reduce the size of the document vectors. Our experiments show that for certain cases, it is possible to prune up to 70% of the collection (or, more specifically, underlying document vectors) and still generate a clustering structure that yields the same quality with that of the original collection, in terms of a set o...
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An efficient and low complexity frequency estimation method based on the discrete Fourier transform (DFT) samples is described. The suggested method can operate with an arbitrary window function in the absence or presence of zero-padding. The frequency estimation performance of the suggested method is shown to follow the Cramer-Rao bound closely without any error floor due to estimator bias, even at exceptionally high signal-to-noise-ratio (SNR) values.
Image Annotation With Semi-Supervised Clustering
Sayar, Ahmet; Yarman Vural, Fatoş Tunay (2009-09-16)
Methods developed for image annotation usually make use of region clustering algorithms. Visual codebooks are generated from the region clusters of low level features. These codebooks are then, matched with the words of the text document related to the image, in various ways. In this paper, we supervise the clustering process by using three types of side information. The first one is the topic probability information obtained from the text document associated with the image. The second is the orientation an...
Consensus clustering of time series data
Yetere Kurşun, Ayça; Batmaz, İnci; İyigün, Cem; Department of Scientific Computing (2014)
In this study, we aim to develop a methodology that merges Dynamic Time Warping (DTW) and consensus clustering in a single algorithm. Mostly used time series distance measures require data to be of the same length and measure the distance between time series data mostly depends on the similarity of each coinciding data pair in time. DTW is a relatively new measure used to compare two time dependent sequences which may be out of phase or may not have the same lengths or frequencies. DTW aligns two time serie...
Low-frequency multilevel fast multipole algorithm using an approximate diagonalization of the Green's function
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We present an approximate diagonalization of the Green's function to implement a stable multilevel fast multipole algorithm (MLFMA) for low-frequency problems. The diagonalization is based on scaled spherical functions, leading to stable computations of translation operators at all distances and for all frequencies. Similar to the conventional diagonalization, shift operators are expressed in terms of complex exponentials, while radiated and incoming fields are expanded in terms of scaled plane waves. Even ...
Citation Formats
İ. S. Altıngövde and O. Ulusoy, “Cluster searching strategies for collaborative recommendation systems,” INFORMATION PROCESSING & MANAGEMENT, pp. 688–697, 2013, Accessed: 00, 2020. [Online]. Available: