2010 TemporalRecommendationonGraphsv

• (Xiang et al., 2010) ⇒ Liang Xiang, Quan Yuan, Shiwan Zhao, Li Chen, Xiatian Zhang, Qing Yang, and Jimeng Sun. (2010). “Temporal Recommendation on Graphs via Long- and Short-term Preference Fusion.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835896

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%22Temporal+recommendation+on+graphs+via+long-+and+short-term+preference+fusion%22+2010
• http://portal.acm.org/citation.cfm?id=1835896&preflayout=flat#citedby

Quotes

Abstract

Accurately capturing user preferences over time is a great practical challenge in recommender systems. Simple correlation over time is typically not meaningful, since users change their preferences due to different external events. User behavior can often be determined by individual's long-term and short-term preferences. How to represent users' long-term and short-term preferences? How to leverage them for temporal recommendation? To address these challenges, we propose Session-based Temporal Graph (STG) which simultaneously models users' long-term and short-term preferences over time. Based on the STG model framework, we propose a novel recommendation algorithm Injected Preference Fusion (IPF) and extend the personalized Random Walk for temporal recommendation. Finally, we evaluate the effectiveness of our method using two real datasets on citations and social bookmarking, in which our proposed method IPF gives 15% - 34% improvement over the previous state-of-the-art.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 TemporalRecommendationonGraphsv	Jimeng Sun Liang Xiang Quan Yuan Shiwan Zhao Li Chen Xiatian Zhang Qing Yang			Temporal Recommendation on Graphs via Long- and Short-term Preference Fusion		KDD-2010 Proceedings		10.1145/1835804.1835896		2010