Structural Balance Theory-based E-commerce Recommendation over Big Rating Data
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 : Pentium Dual Core.
- Hard Disk : 120 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 1 GB
- Operating system : Windows 7.
- Coding Language : JAVA/J2EE
- Tool : Netbeans 7.2.1
- Database : MYSQL
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.