By B C Craft; Murray F Hawkins
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36–55, 2007. c Springer-Verlag Berlin Heidelberg 2007 Nearest-Biclusters Collaborative Filtering with Constant Values 37 that nearest-neighbor algorithms present good performance in terms of accuracy. Nevertheless, their main drawback is that they cannot handle scalability to large volumes of data. On the other hand, model-based algorithms, once they have build the model, present good scalability. However, they have the overhead to build and update the model, and they cannot cover as diverse a user range as the nearest-neighbor algorithms do .
2. Training Set with rating values ≥ Pτ U1 U2 U3 U4 U5 U6 U7 U8 I1 1 0 1 0 0 1 0 0 I2 0 0 0 1 0 0 0 1 I3 0 1 0 0 1 0 1 0 I4 0 0 0 1 0 0 0 1 I5 0 1 0 0 1 0 1 1 I6 0 1 0 1 0 0 0 1 I7 0 0 1 0 0 1 0 0 Fig. 3. , 1 in 1-5 scale. Thus, “negatively” rated items should not contribute to the increase of accuracy. This is the reason that we are interested only in the positive ratings, as shown in Figure 2. Furthermore, as biclustering groups items and users simultaneously, it allows to identify sets of users sharing common preferences across subsets of items.
In: WWW 2004. Proceedings of the 13th international conference on World Wide Web, pp. 482–490. ACM Press, New York (2004) 8. : Outperforming LRU with an adaptive replacement cache algorithm. Computer 37(4), 58–65 (2004) 9. : Smartback: Supporting users in back navigation. In: WWW 2004. Proceedings of the 13th international conference on World Wide Web, pp. 63–71. ACM Press, New York (2004) 10. : Adaptive Web Sites: Cluster Mining and Conceptual Clustering for Index Page Synthesis. PhD thesis, University of Washington (2001) 11.