A Hybrid Method to Solve Data Sparsity in Travel Recommendation Agents Using Fuzzy Logic Approach

Table 9

The results for precision and F1 of all approaches for different Top-N.

TOP-N	EM + HOSVD + ANFIS		EM + ANFIS		Pearson nearest neighbor		SVD		HOSVD + ANFIS
	Precision	F-measure	Precision	F-measure	Precision	F-measure	Precision	F-measure	Precision	F-measure
Top5	0.7519	0.7647	0.6126	0.6363	0.4905	0.5403	0.5184	0.5675	0.6162	0.6447
Top10	0.7550	0.7729	0.6340	0.6464	0.5335	0.5490	0.5552	0.5762	0.6389	0.6472
Top15	0.7551	0.7748	0.6351	0.6899	0.5393	0.5586	0.5656	0.5843	0.6448	0.6962
Top20	0.7561	0.7909	0.6352	0.6988	0.5483	0.5795	0.5667	0.5967	0.6427	0.7013
Top25	0.7581	0.7939	0.6396	0.6994	0.5585	0.5857	0.5772	0.6032	0.6416	0.7067
Top30	0.7584	0.7975	0.6681	0.7183	0.5662	0.5944	0.5795	0.6223	0.6748	0.7248
Top35	0.7667	0.7981	0.6724	0.7200	0.5804	0.6037	0.5944	0.6507	0.6785	0.7201
Top40	0.7700	0.8005	0.6782	0.7239	0.6095	0.6353	0.6265	0.6567	0.6854	0.7294
Top45	0.7894	0.8042	0.6829	0.7240	0.6186	0.6403	0.6327	0.6597	0.6882	0.7246
Top50	0.7969	0.8091	0.6831	0.7249	0.6234	0.6491	0.6357	0.6631	0.6919	0.7291
Top55	0.7984	0.8293	0.6925	0.7304	0.6235	0.6496	0.6451	0.6758	0.7022	0.7339
Top60	0.8017	0.8331	0.6997	0.7321	0.6299	0.6546	0.6461	0.6768	0.7027	0.7337
Top65	0.8078	0.8343	0.7023	0.7356	0.6310	0.6663	0.6489	0.6768	0.7047	0.7375
Top70	0.8240	0.8395	0.7051	0.7477	0.6401	0.6672	0.6492	0.6796	0.7126	0.7497
Top75	0.8244	0.8563	0.7248	0.7541	0.6407	0.6701	0.6495	0.6814	0.7339	0.7549
Top80	0.8261	0.8603	0.7273	0.7638	0.6408	0.6710	0.6531	0.6827	0.7276	0.7711
Top85	0.8586	0.8870	0.7397	0.7655	0.6466	0.6737	0.6546	0.6843	0.7430	0.7709
Top90	0.8640	0.8974	0.7444	0.7657	0.6483	0.6848	0.6567	0.6881	0.7465	0.7726
Top95	0.8739	0.9009	0.7608	0.7723	0.6493	0.6855	0.6586	0.6906	0.7707	0.7807
Top100	0.8950	0.9112	0.7703	0.7835	0.6534	0.6878	0.6599	0.6996	0.7764	0.7889