TY - GEN
T1 - Correlation-Based Pre-Filtering for Context-Aware Recommendation
AU - Ferdousi, Zahra Vahidi
AU - Colazzo, Dario
AU - Negre, Elsa
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/2
Y1 - 2018/10/2
N2 - With the increasing use of connected devices and IoT, users' contextual information is more and more available and used in different information systems. One of the domains where the use of contextual information is promising is that of recommendation. As a matter of fact, context-aware recommender systems (CARSs) have demonstrated that taking contextual information about users into account can improve the effectiveness of recommendation, by generating more relevant recommendations to the users in their specific contextual situation. In this paper we propose a new context representation and approach to integrate this kind of information into a recommender system. We make a strong representation of the context, based on the influence of context on ratings, calculated using the Pearson Correlation Coefficient. We do a pre-filtering recommendation based on this representation. Our evaluations demonstrate that our approach can outperforms the state of the art.
AB - With the increasing use of connected devices and IoT, users' contextual information is more and more available and used in different information systems. One of the domains where the use of contextual information is promising is that of recommendation. As a matter of fact, context-aware recommender systems (CARSs) have demonstrated that taking contextual information about users into account can improve the effectiveness of recommendation, by generating more relevant recommendations to the users in their specific contextual situation. In this paper we propose a new context representation and approach to integrate this kind of information into a recommender system. We make a strong representation of the context, based on the influence of context on ratings, calculated using the Pearson Correlation Coefficient. We do a pre-filtering recommendation based on this representation. Our evaluations demonstrate that our approach can outperforms the state of the art.
KW - Collaborative Filtering
KW - Context-Aware Recommender System
KW - Contextual Information Integration
KW - Contextual Pre-Filtering
KW - Matrix Factorization
UR - https://www.scopus.com/pages/publications/85056473770
U2 - 10.1109/PERCOMW.2018.8480278
DO - 10.1109/PERCOMW.2018.8480278
M3 - Conference contribution
AN - SCOPUS:85056473770
T3 - 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018
SP - 89
EP - 94
BT - Proceedings - 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018
Y2 - 19 March 2018 through 23 March 2018
ER -