id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_w7p2v35cpfenzaihw4nyphoxeq	P SYMEONIDIS	Collaborative recommender systems: Combining effectiveness and efficiency	2008	19	.pdf	application/pdf	13175	1203	66	Recommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. model-based algorithms, which recommend by first developing a model of user ratings. Notably, we examine several similarity measures, various criteria for generating the recommendation list, the appropriateness of evaluation metrics, Konstan, and Riedl (2002) weight similarities by the number of common ratings between users/items. • Stage 2: top-N list generation with algorithms that construct a list of best items recommendations for a user. Past/future data: In real-world applications, recommendations are derived only from the currently available ratings of the test user. example, if we use two past-items, we have to compute similarities based only on the provided two ratings and we of U1 and U2 users, when only co-rated items are considered, then the similarity measure will be computed based First, we examine user-based CF algorithms and compare the existing Pearson similarity and WS measures	./cache/work_w7p2v35cpfenzaihw4nyphoxeq.pdf	./txt/work_w7p2v35cpfenzaihw4nyphoxeq.txt