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» Explaining collaborative filtering recommendations
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EMNLP
2006
13 years 9 months ago
Relevance Feedback Models for Recommendation
We extended language modeling approaches in information retrieval (IR) to combine collaborative filtering (CF) and content-based filtering (CBF). Our approach is based on the anal...
Masao Utiyama, Mikio Yamamoto
WECWIS
2009
IEEE
159views ECommerce» more  WECWIS 2009»
14 years 2 months ago
Context-Aware Recommendation by Aggregating User Context
— Traditional recommendation approaches do not consider the changes of user preferences according to context. As a result, these approaches consider the user’s overall preferen...
Dongmin Shin, Jae-won Lee, Jongheum Yeon, Sang-goo...
AIR
2005
120views more  AIR 2005»
13 years 7 months ago
Rascal: A Recommender Agent for Agile Reuse
As software organisations mature, their repositories of reusable software components from previous projects will also grow considerably. Remaining conversant with all components in...
Frank McCarey, Mel Ó Cinnéide, Nicho...
RECSYS
2009
ACM
14 years 2 months ago
Generating transparent, steerable recommendations from textual descriptions of items
We propose a recommendation technique that works by collecting text descriptions of items and using this textual aura to compute the similarity between items using techniques draw...
Stephen J. Green, Paul Lamere, Jeffrey Alexander, ...
ECIR
2006
Springer
13 years 9 months ago
A User-Item Relevance Model for Log-Based Collaborative Filtering
Abstract. Implicit acquisition of user preferences makes log-based collaborative filtering favorable in practice to accomplish recommendations. In this paper, we follow a formal ap...
Jun Wang, Arjen P. de Vries, Marcel J. T. Reinders