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» Ranking in context-aware recommender systems
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RECSYS
2010
ACM
13 years 7 months ago
List-wise learning to rank with matrix factorization for collaborative filtering
A ranking approach, ListRank-MF, is proposed for collaborative filtering that combines a list-wise learning-to-rank algorithm with matrix factorization (MF). A ranked list of item...
Yue Shi, Martha Larson, Alan Hanjalic
SIGIR
2009
ACM
14 years 4 months ago
Personalized tag recommendation using graph-based ranking on multi-type interrelated objects
Social tagging is becoming increasingly popular in many Web 2.0 applications where users can annotate resources (e.g. Web pages) with arbitrary keywords (i.e. tags). A tag recomme...
Ziyu Guan, Jiajun Bu, Qiaozhu Mei, Chun Chen, Can ...
KDD
2009
ACM
192views Data Mining» more  KDD 2009»
14 years 10 months ago
Learning optimal ranking with tensor factorization for tag recommendation
Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or de...
Steffen Rendle, Leandro Balby Marinho, Alexandros ...
RECSYS
2009
ACM
14 years 4 months ago
Rating aggregation in collaborative filtering systems
Recommender systems based on user feedback rank items by aggregating users’ ratings in order to select those that are ranked highest. Ratings are usually aggregated using a weig...
Florent Garcin, Boi Faltings, Radu Jurca, Nadine J...
WWW
2008
ACM
14 years 10 months ago
Trust-based recommendation systems: an axiomatic approach
High-quality, personalized recommendations are a key feature in many online systems. Since these systems often have explicit knowledge of social network structures, the recommenda...
Reid Andersen, Christian Borgs, Jennifer T. Chayes...