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

Table 8

F1 measures and the precision values for different top-N recommendations (MovieLens 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	0.803	0.813	0.787	0.797	0.771	0.773	0.719	0.721	0.564	0.583
Top-10	0.806	0.817	0.796	0.807	0.782	0.784	0.736	0.739	0.582	0.601
Top-15	0.822	0.832	0.816	0.827	0.802	0.804	0.747	0.749	0.592	0.615
Top-20	0.833	0.844	0.821	0.833	0.809	0.811	0.757	0.76	0.601	0.622
Top-25	0.846	0.857	0.833	0.844	0.823	0.825	0.769	0.77	0.628	0.65
Top-30	0.839	0.849	0.831	0.84	0.819	0.821	0.757	0.762	0.603	0.605
Top-35	0.832	0.843	0.823	0.832	0.806	0.808	0.75	0.751	0.581	0.59
Top-40	0.829	0.84	0.819	0.83	0.801	0.803	0.739	0.741	0.573	0.579
Top-45	0.821	0.835	0.818	0.827	0.793	0.795	0.733	0.732	0.556	0.558
Top-50	0.819	0.832	0.813	0.822	0.783	0.785	0.723	0.722	0.541	0.546