Community Question Answering （CQA） websites have greatly facilitated users＇ lives, with an increasing number of people seeking help and exchanging ideas on the Internet. This newlymerged community features two char...Community Question Answering （CQA） websites have greatly facilitated users＇ lives, with an increasing number of people seeking help and exchanging ideas on the Internet. This newlymerged community features two characteristics： social relations and an ask-reply mechanism. As users＇ behaviours and social statuses play a more important role in CQA services than traditional answer retrieving websites, researchers＇ concerns have shifted from the need to passively find existing answers to actively seeking potential reply providers that may give answers in the near future. We analyse datasets derived from an online CQA system named ＂Quora＂, and observed that compared with traditional question answering services, users tend to contribute replies rather than questions for help in the CQA system. Inspired by the findings, we seek ways to evaluate the users＇ ability to offer prompt and reliable help, taking into account activity, authority and social reputation char- acteristics. We propose a hybrid method that is based on a Question-User network and social network using optimised PageRank algorithm. Experimental results show the efficiency of the proposed method for ranking potential answer-providers.展开更多
Recommendation systems have been extensively studied over the last decade in various domains. It has been considered a powerful tool for assisting business owners in promoting sales and helping users with decision-mak...Recommendation systems have been extensively studied over the last decade in various domains. It has been considered a powerful tool for assisting business owners in promoting sales and helping users with decision-making when given numerous choices. In this paper, we propose a novel Graph-based Context-Aware Recommendation Systems with Knowledge Graph to analyse and predict users’ behaviours, i.e., making recommendations based on historical events and their implicit associations. The model incorporates contextual information extracted from both users’ historical behaviours and events relations, where the contexts have been modelled as knowledge graphs. By leveraging the advantages offered from the knowledge graph, events dependencies and their subtle relations can be established and have been introduced in the recommendation process. Experimental results indicate that the proposed approach can outperform the state-of-the-art algorithms and achieve more accurate recommendations.展开更多
基金supported by the Fundamental Research Funds for the Central Universitiesthe National Natural Science Foundation of China under Grant No.61271041+2 种基金the National Basic Research Program of China (973 Program) under Grant No.2009CB320504the iCore Integrated Project under Grant No.287708the National Scienceand Technology Major Project under Grants No.2012ZX03005008-001,No.2012ZX03002008
文摘Community Question Answering （CQA） websites have greatly facilitated users＇ lives, with an increasing number of people seeking help and exchanging ideas on the Internet. This newlymerged community features two characteristics： social relations and an ask-reply mechanism. As users＇ behaviours and social statuses play a more important role in CQA services than traditional answer retrieving websites, researchers＇ concerns have shifted from the need to passively find existing answers to actively seeking potential reply providers that may give answers in the near future. We analyse datasets derived from an online CQA system named ＂Quora＂, and observed that compared with traditional question answering services, users tend to contribute replies rather than questions for help in the CQA system. Inspired by the findings, we seek ways to evaluate the users＇ ability to offer prompt and reliable help, taking into account activity, authority and social reputation char- acteristics. We propose a hybrid method that is based on a Question-User network and social network using optimised PageRank algorithm. Experimental results show the efficiency of the proposed method for ranking potential answer-providers.
文摘Recommendation systems have been extensively studied over the last decade in various domains. It has been considered a powerful tool for assisting business owners in promoting sales and helping users with decision-making when given numerous choices. In this paper, we propose a novel Graph-based Context-Aware Recommendation Systems with Knowledge Graph to analyse and predict users’ behaviours, i.e., making recommendations based on historical events and their implicit associations. The model incorporates contextual information extracted from both users’ historical behaviours and events relations, where the contexts have been modelled as knowledge graphs. By leveraging the advantages offered from the knowledge graph, events dependencies and their subtle relations can be established and have been introduced in the recommendation process. Experimental results indicate that the proposed approach can outperform the state-of-the-art algorithms and achieve more accurate recommendations.
