Personalized search utilizes user preferences to optimize search results,and most existing studies obtain user preferences by analyzing user behaviors in search engines that provide click-through data.However,the beha...Personalized search utilizes user preferences to optimize search results,and most existing studies obtain user preferences by analyzing user behaviors in search engines that provide click-through data.However,the behavioral data are noisy because users often clicked some irrelevant documents to find their required information,and the new user cold start issue represents a serious problem,greatly reducing the performance of personalized search.This paper attempts to utilize online social network data to obtain user preferences that can be used to personalize search results,mine the knowledge of user interests,user influence and user relationships from online social networks,and use this knowledge to optimize the results returned by search engines.The proposed model is based on a holonic multiagent system that improves the adaptability and scalability of the model.The experimental results show that utilizing online social network data to implement personalized search is feasible and that online social network data are significant for personalized search.展开更多
Users can obtain the information through a basic web searching and find the answer to the questions directly,but maybe the expected answer does not exist.Besides,we do not know the update of new information in time.Th...Users can obtain the information through a basic web searching and find the answer to the questions directly,but maybe the expected answer does not exist.Besides,we do not know the update of new information in time.The online social networking services spread quickly and store many user data,but these data are worth less and may be unreliable answer to users’ questions.Users can obtain the simple answer but can not expect more additional information in knowledge question-answering(QA)system.In this paper,we design the system with the advantages of knowledge QA system,web searching and characteristics of social networking service for providing social network channel based on the query and answer without users’ contact network.The user can obtain real-time answers by the user network interested in users’ querires through the network channel of this system,get the additional information effectively and share it with others in the social network channel in this system.展开更多
Among mobile users, ad-hoc social network (ASN) is becoming a popular platform to connect and share their interests anytime anywhere. Many researchers and computer scientists investigated ASN architecture, implementat...Among mobile users, ad-hoc social network (ASN) is becoming a popular platform to connect and share their interests anytime anywhere. Many researchers and computer scientists investigated ASN architecture, implementation, user experience, and different profile matching algorithms to provide better user experience in ad-hoc social network. We emphasize that strength of an ad-hoc social network depends on a good profile-matching algorithm that provides meaningful friend suggestions in proximity. Keeping browsing history is a good way to determine user’s interest, however, interests change with location. This paper presents a novel profile-matching algorithm for automatically building a user profile based on dynamic GPS (Global Positing System) location and browsing history of users. Building user profile based on GPS location of a user provides benefits to ASN users as this profile represents user’s dynamic interests that keep changing with location e.g. office, home, or some other location. Proposed profile-matching algorithm maintains multiple local profiles based on location of mobile device.展开更多
We investigate the navigation process on a variant of the Watts-Strogatz small-world network model with local information. In the network construction, each vertex of an N x N square lattice sends out a long-range lin...We investigate the navigation process on a variant of the Watts-Strogatz small-world network model with local information. In the network construction, each vertex of an N x N square lattice sends out a long-range link with probability p. The other end of the link falls on a randomly chosen vertex with probability proportional to r^-α, where r is the lattice distance between the two vertices, and α ≥ 0. The average actual path length, i.e. the expected number of steps for passing messages between randomly chosen vertex pairs, is found to scale as a power-law function of the network size N^β, except when α is close to a specific value value, which gives the highest efficiency of message navigation. For a finite network, the exponent β depends on both α and p, and p αmin drops to zero at a critical value of p which depends on N. When the network size goes to infinity,β depends only only on α, and αmin is equal to the network dimensionality.展开更多
基金supported by the National Natural Science Foundation of China (61972300, 61672401, 61373045, and 61902288,)the Pre-Research Project of the “Thirteenth Five-Year-Plan” of China (315***10101 and 315**0102)
文摘Personalized search utilizes user preferences to optimize search results,and most existing studies obtain user preferences by analyzing user behaviors in search engines that provide click-through data.However,the behavioral data are noisy because users often clicked some irrelevant documents to find their required information,and the new user cold start issue represents a serious problem,greatly reducing the performance of personalized search.This paper attempts to utilize online social network data to obtain user preferences that can be used to personalize search results,mine the knowledge of user interests,user influence and user relationships from online social networks,and use this knowledge to optimize the results returned by search engines.The proposed model is based on a holonic multiagent system that improves the adaptability and scalability of the model.The experimental results show that utilizing online social network data to implement personalized search is feasible and that online social network data are significant for personalized search.
基金Industrial Strategic Technology Development Program,Development of a Cognitive Planning and Learning Model for Mobile Platforms(No.10035348) funded by MKE(the Ministry of Knowledge Economy),Korea
文摘Users can obtain the information through a basic web searching and find the answer to the questions directly,but maybe the expected answer does not exist.Besides,we do not know the update of new information in time.The online social networking services spread quickly and store many user data,but these data are worth less and may be unreliable answer to users’ questions.Users can obtain the simple answer but can not expect more additional information in knowledge question-answering(QA)system.In this paper,we design the system with the advantages of knowledge QA system,web searching and characteristics of social networking service for providing social network channel based on the query and answer without users’ contact network.The user can obtain real-time answers by the user network interested in users’ querires through the network channel of this system,get the additional information effectively and share it with others in the social network channel in this system.
文摘Among mobile users, ad-hoc social network (ASN) is becoming a popular platform to connect and share their interests anytime anywhere. Many researchers and computer scientists investigated ASN architecture, implementation, user experience, and different profile matching algorithms to provide better user experience in ad-hoc social network. We emphasize that strength of an ad-hoc social network depends on a good profile-matching algorithm that provides meaningful friend suggestions in proximity. Keeping browsing history is a good way to determine user’s interest, however, interests change with location. This paper presents a novel profile-matching algorithm for automatically building a user profile based on dynamic GPS (Global Positing System) location and browsing history of users. Building user profile based on GPS location of a user provides benefits to ASN users as this profile represents user’s dynamic interests that keep changing with location e.g. office, home, or some other location. Proposed profile-matching algorithm maintains multiple local profiles based on location of mobile device.
基金Supported by the National Natural Science Foundation of China under Grant No 10375008, and the National Basic Research Programme of China under Grant No 2003CB716302
文摘We investigate the navigation process on a variant of the Watts-Strogatz small-world network model with local information. In the network construction, each vertex of an N x N square lattice sends out a long-range link with probability p. The other end of the link falls on a randomly chosen vertex with probability proportional to r^-α, where r is the lattice distance between the two vertices, and α ≥ 0. The average actual path length, i.e. the expected number of steps for passing messages between randomly chosen vertex pairs, is found to scale as a power-law function of the network size N^β, except when α is close to a specific value value, which gives the highest efficiency of message navigation. For a finite network, the exponent β depends on both α and p, and p αmin drops to zero at a critical value of p which depends on N. When the network size goes to infinity,β depends only only on α, and αmin is equal to the network dimensionality.
