LITMIR.BIZ

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0.841 0.7564 0.6335 U2U-Euclidean 0.886 0.8337 0.7254 10 50 Y 0.841 0.7269 0.6346 U2U-Pearson 0.964 1.1222 0.9789 10 50 N 0.8364 0.8007 0.6276 Static-Multi-U2U N.A. 0.798 N.A. 30 10 Y 0.8461 0.7677 0.712 Multi-I2I 0.864 0.8245 0.811 30 10 N 0.8473 0.7722 0.7122 I2I-Euclidean 0.8745 0.8429 0.8117 30 50 Y 0.8474 0.7743 0.7101 I2I-Pearson 1.1794 0.8644 0.9679 30 50 N 0.8494 0.8003 0.714 Static-Multi-I2I N.A. 0.8474 N.A. 80 10 Y 0.8579 0.7971 0.7584 RatingSGD 0.8409 0.745 0.8859 80 10 N 0.8592 0.7953 0.7554 ParallelSGD 0.8409 0.7449 0.8852 80 50 Y 0.859 0.7907 0.7544 ALSWR 0.9545 0.9053 1.0354 80 50 N 0.8597 0.7995 0.7544

       Result of MCRS Item–Based CF

       Top 10 Main Aspects Extracted

Configuration	Dataset		Yelp	Place, food, service, restaurant, price menu, staff, drink, and lunch
#asp.	Sub-asp	Yelp	TripAdvisor		Amazon
10	Y	0.864	0.8245	0.811		TripAdvisor	Hotel, room, staff, location, service, breakfast, restaurant, bathroom, price, view
10	N	0.8643	0.8252	0.8117
50	Y	0.8641	0.8254	0.8118		Amazon	game, graphic, story, character, player price, gameplay, controller level, and music
50	N	0.8648	0.826	0.8124

It is noted that, to get better efficiency, we need to configure all the datasets in top 10 neighbors.

In the next step, they compared the algorithm with matrix factorization (MF) algorithms for getting better baselines. Besides, it is specified that TripAdvisor dataset has an unchangeable set of six features. For every analysis like “Cleanliness”, “location”, “value”, “service”, “sleep quality”, and “overall”. They have differentiated their way with a MCRS algorithm that depends on those aspects. In Table 3.5, we can see that their method gets the better of against all the baselines. These results surely established that their

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