TITLE:
A New Aware-Context Collaborative Filtering Approach by Applying Multivariate Logistic Regression Model into General User Pattern
AUTHORS:
Loc Nguyen
KEYWORDS:
Aware-Context Collaborative Filtering, Logistic Regression Model
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.4 No.3,
August
18,
2016
ABSTRACT: Traditional collaborative filtering (CF) does not take into account contextual factors such as time,
place, companion, environment, etc. which are useful information around users or relevant to recommender
application. So, recent aware-context CF takes advantages of such information in order
to improve the quality of recommendation. There are three main aware-context approaches:
contextual pre-filtering, contextual post-filtering and contextual modeling. Each approach has individual
strong points and drawbacks but there is a requirement of steady and fast inference
model which supports the aware-context recommendation process. This paper proposes a new
approach which discovers multivariate logistic regression model by mining both traditional rating
data and contextual data. Logistic model is optimal inference model in response to the binary
question “whether or not a user prefers a list of recommendations with regard to contextual condition”.
Consequently, such regression model is used as a filter to remove irrelevant items from
recommendations. The final list is the best recommendations to be given to users under contextual
information. Moreover the searching items space of logistic model is reduced to smaller set of
items so-called general user pattern (GUP). GUP supports logistic model to be faster in real-time
response.