CSCF: Clustering based-Approach for Social Collaborative Filtering

被引：0

作者：

Chekkai, Nassira ^{[1
]}

Tahraoui, Mohammed Amin ^{[2
]}

Hamadouche, Mohamed Ait ^{[3
]}

Chikhi, Salim ^{[1
]}

Kheddouci, Hamamache ^{[4
]}

Meshoul, Souham ^{[1
]}

Bouaziz, Amira ^{[1
]}

机构：

[1] Univ Abdelhamid Mehri, Constantine 2, Algeria

[2] Hassiba Benbouali Chlef Univ, Ouled Fares, Algeria

[3] Univ Sci & Technol, Oran, Algeria

[4] Lyon 1 Univ, LIRIS Lab, Lyon, France

来源：

PROCEEDINGS OF 2017 FIRST INTERNATIONAL CONFERENCE ON EMBEDDED & DISTRIBUTED SYSTEMS (EDIS 2017) | 2017年

关键词：

Cold Start Problem; Delegates; Graph theory; Recommender System; Social Collaborative Filtering; CENTRALITY;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Nowadays, Collaborative Filtering (CF) has become a widely used technique in the field of recommender systems. It aims to recommend items that are relevant to the tastes and preferences of the users based on the social relationships between them. One crucial issue in CF is the Cold Start Recommendation which includes two key aspects: new user and new item. Cold user is a new comer who enters the system and cannot get relevant items, while cold item is a new item that cannot be recommended since it has no ratings yet. In this paper, we present "CSCF" a graph-based approach for social collaborative filtering. CSCF offers many interactive tasks aiming to improve the user satisfaction and solves the cold start challenges by identifying the most effective delegates with clustering. Computational results are demonstrated to confirm the effectiveness of our proposed approach.

引用

页码：33 / 38

页数：6

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