Diversified Recommendation Based on Light Graph Convolution Networks and Implicit Feedback Enhancement
In recent years,researchers have been striving to improve the accuracy of recommendation systems while ignoring the critical impact of diversity on user satisfaction.Most current diversified recommendation algorithms impose diversity constraints after the accuracy candidate list generated by traditional post-processing algorithms.However,this decoupled design consistently results in a sub-optimal system.Meanwhile,although the effectiveness of recommendation algorithms using graph convolution networks(GCN)in improving recommendation accuracy has been demonstrated,the applicability and diversity design for recom-mendation remain neglected.In addition,recommendation algorithms employing a single explicit user feedback of purchasing inev-itably fall into"recommendation overload".Therefore,an end-to-end diversified light graph convolution networks recommendation(DLGCRec)is proposed to overcome these drawbacks.Firstly,GCN is simplified to light graph convolution networks(LGCN)to be suitable for recommendation,and LGCN is utilized to push diversity upstream to the recommendation process of accuracy match.Then,in the sampling phase of LGCN,diversity-boosted negative sampling that introduces user implicit feedback is utilized to explore the user's diversified preferences.Finally,a multi-layer feature fusion strategy is utilized to capture the complete fea-ture embedding of the nodes to enhance the recommendation performance.Experimental results on real datasets validate the effec-tiveness of DLGCRec in applying in recommendations and enhancing diversity.Further ablation studies confirm that DLGCRec ef-fectively mitigates the accuracy-diversity dilemma.
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