TY - JOUR
T1 - Gradually adaptive recommendation based on semantic mapping of users′ interest correlations
AU - Chen, Jian
AU - Zhou, Xiaokang
AU - Jin, Qun
PY - 2016/1/25
Y1 - 2016/1/25
N2 - SUMMARY In this paper, we propose a gradually adaptive recommendation model based on the combination of both users' commonalities and individualities that depend on the semantic mapping of users' interest correlations. We analyze users' information access behaviors and histories to extract users' interests and trace their transitions. In details, according to a set of bookmark tags classified by a semantic means, the pages accessed by users are assigned into several tag classes, which will finally be clustered into different groups in accordance with the types of interests that belong to two categories: personal and common interests, respectively. Based on the detection of users' interest focus transitions through interactions between users, we provide a series of information seeking actions in sequence to the target users. Besides, according to the reference groups which are defined to describe different relations with the target users, the successful experience is extracted and recommended. After the description of the definitions and measures, the mechanism to infer the interest focus, the system architecture and experimental evaluation results are described and demonstrated.
AB - SUMMARY In this paper, we propose a gradually adaptive recommendation model based on the combination of both users' commonalities and individualities that depend on the semantic mapping of users' interest correlations. We analyze users' information access behaviors and histories to extract users' interests and trace their transitions. In details, according to a set of bookmark tags classified by a semantic means, the pages accessed by users are assigned into several tag classes, which will finally be clustered into different groups in accordance with the types of interests that belong to two categories: personal and common interests, respectively. Based on the detection of users' interest focus transitions through interactions between users, we provide a series of information seeking actions in sequence to the target users. Besides, according to the reference groups which are defined to describe different relations with the target users, the successful experience is extracted and recommended. After the description of the definitions and measures, the mechanism to infer the interest focus, the system architecture and experimental evaluation results are described and demonstrated.
KW - data mining
KW - gradual adaptation
KW - information recommendation
KW - semantic mapping of interest correlations
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U2 - 10.1002/dac.2835
DO - 10.1002/dac.2835
M3 - Article
AN - SCOPUS:84957441914
VL - 29
SP - 341
EP - 361
JO - International journal of digital and analog communication systems
JF - International journal of digital and analog communication systems
SN - 1074-5351
IS - 2
ER -