RECSYS 2009 - PDF Preprint Papers

RECSYS 2009 ACM Conference On Recommender Systems

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RECSYS
2009
ACM

324views Control Systems» more RECSYS 2009»

Recommending new movies: even a few ratings are more valuable than metadata

13 years 11 months ago

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In the Netﬂix Prize competition many new collaborative ﬁltering (CF) approaches emerged, which are excellent in optimizing the RMSE of the predictions. Matrix factorization (M...

István Pilászy, Domonkos Tikk

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Id	Views	Title	Status

1	324	Recommending new movies: even a few ratings are more valuable than metadata	claim
2	297	Applying relevant set correlation clustering to multi-criteria recommender systems	claim
3	214	Latent dirichlet allocation for tag recommendation	claim
4	207	Matching information content with music	claim
5	195	A semantic framework for personalized ad recommendation based on advanced textual analysis	claim
6	194	Learning to recommend with trust and distrust relationships	claim
7	193	FeedbackTrust: using feedback effects in trust-based recommendation systems	claim
8	184	iTag: a personalized blog tagger	claim
9	183	Effective diverse and obfuscated attacks on model-based recommender systems	claim
10	182	Regret-based optimal recommendation sets in conversational recommender systems	claim
11	177	Using twitter to recommend real-time topical news	claim
12	174	Rating aggregation in collaborative filtering systems	claim
13	173	Ordering innovators and laggards for product categorization and recommendation	claim
14	169	Acceptance issues of personality-based recommender systems	claim
15	162	Conceptual recommender system for CiteSeerX	claim
16	159	Pairwise preference regression for cold-start recommendation	claim
17	157	Increasing engagement through early recommender intervention	claim
18	157	Critiquing recommenders for public taste products	claim
19	155	Context-based splitting of item ratings in collaborative filtering	claim
20	155	The impact of ambiguity and redundancy on tag recommendation in folksonomies	claim
21	150	FriendSensing: recommending friends using mobile phones	claim
22	149	TagiCoFi: tag informed collaborative filtering	claim
23	149	Ensemble methods for improving the performance of neighborhood-based collaborative filtering	claim
24	148	Assessment of conversation co-mentions as a resource for software module recommendation	claim
25	146	MoviExplain: a recommender system with explanations	claim
26	145	Learning to recommend helpful hotel reviews	claim
27	144	Putting recommendations on the map: visualizing clusters and relations	claim
28	143	Uncovering functional dependencies in MDD-compiled product catalogues	claim
29	141	A partial-order based active cache for recommender systems	claim
30	141	Improving rating estimation in recommender systems using aggregation- and variance-based hierarchical models	claim
31	140	Collaborative prediction and ranking with non-random missing data	claim
32	134	Rate it again: increasing recommendation accuracy by user re-rating	claim
33	131	Measuring predictive capability in collaborative filtering	claim
34	125	Harnessing the power of "favorites" lists for recommendation systems	claim
35	123	Using a trust network to improve top-N recommendation	claim
36	121	An incentive-based architecture for social recommendations	claim
37	119	Recommender systems for the conference paper assignment problem	claim
38	119	Getting recommender systems to think outside the box	claim
39	117	Recommendations with prerequisites	claim
40	117	A spatio-temporal approach to collaborative filtering	claim
41	114	Preserving privacy in collaborative filtering through distributed aggregation of offline profiles	claim
42	107	Generating transparent, steerable recommendations from textual descriptions of items	claim
43	105	Preference elicitation with subjective features	claim

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