Dealing with Pure New User Cold-Start Problem in Recommendation System Based on Linked Open Data and Social Network Features

Table 9

F1 measures and the precision values for different top-N recommendations (Yahoo Webscope dataset).

Top N	Proposed system		Method A		Method B		Method C		Method D
	Precision	F1 metric	Precision	F1 metric	Precision	F1 metric	Precision	F1 metric	Precision	F1 metric
Top-5	0.768	0.786	0.757	0.767	0.714	0.727	0.669	0.694	0.494	0.516
Top-10	0.786	0.798	0.766	0.777	0.737	0.757	0.683	0.707	0.517	0.534
Top-15	0.803	0.814	0.783	0.792	0.74	0.764	0.688	0.709	0.528	0.549
Top-20	0.808	0.817	0.786	0.797	0.747	0.771	0.692	0.711	0.536	0.557
Top-25	0.802	0.817	0.788	0.797	0.749	0.776	0.697	0.714	0.542	0.565
Top-30	0.824	0.832	0.789	0.801	0.757	0.779	0.704	0.724	0.551	0.571
Top-35	0.826	0.835	0.791	0.803	0.761	0.785	0.715	0.727	0.564	0.582
Top-40	0.825	0.838	0.799	0.805	0.764	0.791	0.721	0.734	0.571	0.594
Top-45	0.827	0.839	0.809	0.814	0.772	0.793	0.727	0.744	0.585	0.608
Top-50	0.839	0.843	0.815	0.821	0.784	0.798	0.739	0.762	0.617	0.631