TY - GEN
T1 - A Context Features Selecting and Weighting Methods for Context-Aware Recommendation
AU - Zammali, Saloua
AU - Arour, Khedija
AU - Bouzeghoub, Amel
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/21
Y1 - 2015/9/21
N2 - The notion of 'Context' plays a key role in recommender systems. In this respect, many researches have been dedicated for Context-Aware Recommender Systems (CARS). Rating prediction in CARS is being tackled by researchers attempting to recommend appropriate items to users. However, in rating prediction, three thriving challenges still to tackle:(i) context feature's selection, (ii) context feature's weighting, and (iii) users context matching. Context-aware algorithms made a strong assumption that context features are selected in advance and their weights are the same or initialized with random values. After context features weighting, users context matching is required. In current approaches, syntactic measures are used which require an exact matching between features. To address these issues, we propose a novel approach for Selecting and Weighting Context Features (SWCF). The evaluation experiments show that the proposed approach is helpful to improve the recommendation quality.
AB - The notion of 'Context' plays a key role in recommender systems. In this respect, many researches have been dedicated for Context-Aware Recommender Systems (CARS). Rating prediction in CARS is being tackled by researchers attempting to recommend appropriate items to users. However, in rating prediction, three thriving challenges still to tackle:(i) context feature's selection, (ii) context feature's weighting, and (iii) users context matching. Context-aware algorithms made a strong assumption that context features are selected in advance and their weights are the same or initialized with random values. After context features weighting, users context matching is required. In current approaches, syntactic measures are used which require an exact matching between features. To address these issues, we propose a novel approach for Selecting and Weighting Context Features (SWCF). The evaluation experiments show that the proposed approach is helpful to improve the recommendation quality.
KW - Context-aware recommendation
KW - Features selection
KW - Features weighting
U2 - 10.1109/COMPSAC.2015.104
DO - 10.1109/COMPSAC.2015.104
M3 - Conference contribution
AN - SCOPUS:84962192859
T3 - Proceedings - International Computer Software and Applications Conference
SP - 575
EP - 584
BT - Proceedings - 2015 IEEE 39th Annual Computer Software and Applications Conference, COMPSAC 2015
A2 - Ahamed, Sheikh Iqbal
A2 - Chang, Carl K.
A2 - Chu, William
A2 - Crnkovic, Ivica
A2 - Hsiung, Pao-Ann
A2 - Huang, Gang
A2 - Yang, Jingwei
PB - IEEE Computer Society
T2 - 39th Annual IEEE Computer Software and Applications Conference, COMPSAC 2015
Y2 - 1 July 2015 through 5 July 2015
ER -