Original scientific paper
https://doi.org/10.7305/automatika.54-2.258
Impact of the Context Relevancy on Ratings Prediction in a Movie-Recommender System
Ante Odić
; Digital Signal, Image and Video Processing Laboratory, Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, SI-1000 Ljubljana, Slovenia
Marko Tkalčič
orcid.org/0000-0002-0831-5512
; Digital Signal, Image and Video Processing Laboratory, Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, SI-1000 Ljubljana, Slovenia
Jurij F. Tasič
; Digital Signal, Image and Video Processing Laboratory, Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, SI-1000 Ljubljana, Slovenia
Andrej Košir
; Digital Signal, Image and Video Processing Laboratory, Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, SI-1000 Ljubljana, Slovenia
Abstract
Recommender systems are a popular and a highly researched way of helping users get to their desired content in the huge amount of available data, and services online. Understanding the situation in which users consume the items was shown to improve the recommendation process. For that reason, context-aware recommender system (CARS) employs contextual information in order to enhance the user’s model and to improve the recommendations. An issue that is still open is how to decide which pieces of contextual information to acquire and how to incorporate them into CARS, since using irrelevant piece of contextual information could have a negative impact on the recommendations. We propose a methodology for detecting which pieces of contextual information contribute to explaining the variance in the ratings, based on statistical testing. We also inspect the impact of the detected relevant pieces of contextual information on the ratings prediction based on the matrix-factorization algorithm. The experiment was conducted on the MovieAT database. The results showed a significant difference in the ratings prediction using the relevant and the irrelevant pieces of contextual information. We also confirmed the positive impact of the relevant, and negative impact of the irrelevant pieces of contextual information with respect to the uncontextualized model.
Keywords
Personalization; Recommender systems; Context-awareness
Hrčak ID:
103426
URI
Publication date:
2.5.2013.
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