ABSTRACT:
Recommending appropriate product
items to the target user is becoming the key to ensure continuous success of
Ecommerce. Today, many E-commerce systems adopt various recommendation
techniques, e.g., Collaborative Filtering (abbreviated as CF)-based technique,
to realize product item recommendation. Overall, the present CF recommendation
can perform very well, if the target user owns similar friends (user-based CF),
or the product items purchased and preferred by target user own one or more
similar product items (item-based CF). While due to the sparsity
of big rating data in E-commerce, similar friends and similar product items may
be both absent from the user-product purchase network, which lead to a big
challenge to recommend appropriate product items to the target user.
Considering the challenge, we put forward a Structural Balance Theory-based
Recommendation (i.e., SBT-Rec) approach. In the
concrete, (Ⅰ) user-based recommendation:
we look for target user’s “enemy” (i.e., the users having opposite preference
with target user); afterwards, we determine target user’s “possible friends”,
according to “enemy’s enemy is a friend” rule of Structural Balance Theory, and
recommend the product items preferred by “possible friends” of target user to
the target user. (Ⅱ) likewise, for the
product items purchased and preferred by target user, we determine their
“possibly similar product items” based on Structural Balance Theory and
recommend them to the target user. At last, the feasibility of SBT-Rec is validated, through a set of experiments deployed on
MovieLens-1M dataset.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
Lianyong Qi, Xiaolong Xu, Xuyun Zhang, Wanchun Dou, Chunhua Hu, Yuming Zhou, Jiguo Yu, “Structural Balance Theory-based E-commerce
Recommendation over Big Rating Data”, IEEE Transactions on Big Data, 2018.