Computer Science ›› 2019, Vol. 46 ›› Issue (10): 1-6.doi: 10.11896/jsjkx.180901792

• Big Data & Data Science •     Next Articles

Cross-domaing Item Recommendation Algorithm Including Tag Transfer

GE Meng-fan, LIU Zhen, WANG Na-na, TIAN Jing-yu   

  1. (School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China)
  • Received:2018-09-22 Revised:2019-03-10 Online:2019-10-15 Published:2019-10-21

Abstract: Most recommendation algorithms often use cross-domain recommendation technology based on transfer lear-ning and rich data in the auxiliary domain to solve the problems such as data sparse commonly existing in traditional single domain recommendation.However,if the transtered knowledge is relatively simple without combining user beha-vior,it will lead to the problems such as negative transfer and poor recommendation results.Therefore,it is possible to combine other knowledge to assist the learning tasks in target domain.Using user heterogeneous behavior to improve recommendation results is one of the emerging research hotspots in recent years.For user data,tags are related to the real user preferences,which can reflect some implicit features of user or item.In light of this,this paper proposed a cross-domain item recommendation algorithm ITTCF(Item-based Tag Transfer Collaborative Filtering)based on tag transfer.Instead of single auxiliary moded of performing mining and migration for rating pattern in cross-domain recommendation,this method combines user behavior feedback and numeric ratings,and fuses two typical user behaviors:ra-ting patterns and tags.Experimental results on multiple datasets show that ITTCF has lower RMSE and MAE values,and its performance is 1.61% to 6.67% and 1.97% to 8.83% higher respectively than traditional algorithms.

Key words: Cross-domain recommendation, Item-based collaborative, Tag, Transfer learning

CLC Number: 

  • TP391.9
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