TY - JOUR
AU - Mohammed, Alshahrani
AB - AbstractIn Context-Aware Recommendation, contextual information is usually represented as attributes (or features). Most approaches utilize the static values of attributes to facilitate rating predictions—and some try to capture all the attribute interactions to generate more accurate recommendations. However, attribute connotations vary significantly across different users or items, which means that static attributes can become powerless to express personalized preferences. Moreover, capturing all attribute interactions is pointless because some interactions benefit prediction, while others reduce the performance. In this paper, we refine the attributes dynamically through three interacting aspects: attribute types, user preferences and item relations. By following this process, called Attribute Boosting (AB), attribute interactions are targeted to provide more accurate rating predictions. Furthermore, Gradient Boosted Regression Trees (GBRT) are tailored to act as the Global Boosting (GB) part of our model to create personalized global biases that are separated from the AB process. Finally, the prediction is generated from the combination of these two components. The experimental results demonstrate that the Attribute and GB approach addresses the limitations of fixed attributes, outperforms other representative methods and is flexible by adjusting the attribute type granularities.
TI - Attribute and Global Boosting: A Rating Prediction Method in Context-Aware Recommendation
JF - The Computer Journal
DO - 10.1093/comjnl/bxw016
DA - 2017-07-01
UR - https://www.deepdyve.com/lp/oxford-university-press/attribute-and-global-boosting-a-rating-prediction-method-in-context-1xlj8i02GS
SP - 957
EP - 968
VL - 60
IS - 7
DP - DeepDyve
ER -