Telecom infrastructure sharing might be a new concept introduced to China recently,but it is standard practice globally.Based on the common prac-tice experiences from foreign countries,the paper ana-lyzes relevant iss...Telecom infrastructure sharing might be a new concept introduced to China recently,but it is standard practice globally.Based on the common prac-tice experiences from foreign countries,the paper ana-lyzes relevant issues of telecom infrastructure sharing in the Chinese context,and then proposals several recom-mendations for implementation of the regulatory policy.展开更多
Nowadays open source software becomes highly popular and is of great importance for most software engi- neering activities. To facilitate software organization and re- trieval, tagging is extensively used in open sour...Nowadays open source software becomes highly popular and is of great importance for most software engi- neering activities. To facilitate software organization and re- trieval, tagging is extensively used in open source communi- ties. However, finding the desired software through tags in these communities such as Freecode and ohloh is still chal- lenging because of tag insufficiency. In this paper, we propose TRG （tag recommendation based on semantic graph）, a novel approach to discovering and enriching tags of open source software. Firstly, we propose a semantic graph to model the semantic correlations between tags and the words in software descriptions. Then based on the graph, we design an effec- tive algorithm to recommend tags for software. With com- prehensive experiments on large-scale open source software datasets by comparing with several typical related works, we demonstrate the effectiveness and efficiency of our method in recommending proper tags.展开更多
The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that different products take...The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that different products take different time periods for conversion. The ＂conversion＂ here refers to actu- ally a more general set of pre-defined actions, including for example purchases or registrations in recommendation and advertising systems. The mismatch between the product＇s ac- tual conversion period and the application＇s target conversion period has been the subtle culprit compromising many exist- ing recommendation algorithms. The challenging question： what products should be recom- mended for a given time period to maximize conversion--is what has motivated us in this paper to propose a rank-based time-aware conversion prediction model （rTCP）, which con- siders both recommendation relevance and conversion time. We adopt lifetime models in survival analysis to model the conversion time and personalize the temporal prediction by incorporating context information such as user preference. A novel mixture lifetime model is proposed to further accom- modate the complexity of conversion intervals. Experimental results on two real-world data sets illustrate the high good- ness of fit of our proposed model rTCP and demonstrate its effectiveness in time-aware conversion rate prediction for ad- vertising and product recommendation.展开更多
文摘Telecom infrastructure sharing might be a new concept introduced to China recently,but it is standard practice globally.Based on the common prac-tice experiences from foreign countries,the paper ana-lyzes relevant issues of telecom infrastructure sharing in the Chinese context,and then proposals several recom-mendations for implementation of the regulatory policy.
文摘Nowadays open source software becomes highly popular and is of great importance for most software engi- neering activities. To facilitate software organization and re- trieval, tagging is extensively used in open source communi- ties. However, finding the desired software through tags in these communities such as Freecode and ohloh is still chal- lenging because of tag insufficiency. In this paper, we propose TRG （tag recommendation based on semantic graph）, a novel approach to discovering and enriching tags of open source software. Firstly, we propose a semantic graph to model the semantic correlations between tags and the words in software descriptions. Then based on the graph, we design an effec- tive algorithm to recommend tags for software. With com- prehensive experiments on large-scale open source software datasets by comparing with several typical related works, we demonstrate the effectiveness and efficiency of our method in recommending proper tags.
文摘The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that different products take different time periods for conversion. The ＂conversion＂ here refers to actu- ally a more general set of pre-defined actions, including for example purchases or registrations in recommendation and advertising systems. The mismatch between the product＇s ac- tual conversion period and the application＇s target conversion period has been the subtle culprit compromising many exist- ing recommendation algorithms. The challenging question： what products should be recom- mended for a given time period to maximize conversion--is what has motivated us in this paper to propose a rank-based time-aware conversion prediction model （rTCP）, which con- siders both recommendation relevance and conversion time. We adopt lifetime models in survival analysis to model the conversion time and personalize the temporal prediction by incorporating context information such as user preference. A novel mixture lifetime model is proposed to further accom- modate the complexity of conversion intervals. Experimental results on two real-world data sets illustrate the high good- ness of fit of our proposed model rTCP and demonstrate its effectiveness in time-aware conversion rate prediction for ad- vertising and product recommendation.
