Purpose： This article reports on an experiment that tested community members＇ collaborative information seeking （CIS） behavior, with an emphasis on how community type and task difficulty can affect user behavior a...Purpose： This article reports on an experiment that tested community members＇ collaborative information seeking （CIS） behavior, with an emphasis on how community type and task difficulty can affect user behavior and user awareness in collaboration.Design/methodology/approach： We carried out a laboratory study with 18 participants in 9 pairs using an experimental CIS system. Data were collected from questionnaires, Web logs and semi-structured interviews. Descriptive statistics and two-way analysis of variance （ANOVA） were used for data analysis. Findings： Compared with non-community members, community participants had a better understanding of search tasks and were aware of the ways of completing tasks successfully. They did not depend on the information retrieval system when constructing search queries and would adopt diversified cooperation strategies. They were more likely to recommend information to their partners. However, no significant difference was found between subject- based community and interest-based community in CIS practices and user awareness in collaboration. In addition, task difficulty only influenced user preference of issuing queries and confidence of completing search tasks. Research limitations： Our work was limited by the community type we chose and the small group size, which could affect the generalizability of our findings and should be addressed in future studies. Practical implications： The study results will help inform information system designers as they design collaborative systems to facilitate social communication in the information seeking process. Originality/value： Few studies have investigated community participants＇ information seeking practices. This study provides insights into community-based CIS behavior. The findings will help us understand social interactions among community members during their information seeking process.展开更多
Mining the content from an information database provides challenging solutions to the industry experts and researchers, due to the overcrowded information in huge data. In web searching, the information retrieved is n...Mining the content from an information database provides challenging solutions to the industry experts and researchers, due to the overcrowded information in huge data. In web searching, the information retrieved is not an appropriate, because it gives ambiguous information for the user query, and the user cannot get relevant information within the stipulated time. To overcome these issues, we propose a new methodology for information retrieval EPCRR by providing the top most exact information to the user, by using the collaborative clustered automated filter which makes use of the collaborative data set and filter works on the prediction by providing the highest ranking for the exact data retrieved. The retrieval works on the basis of recommendation of data which consists of relevant data set with highest priority from the cluster of data which is on high usage. In this work, we make use of the automated wrapper which works similar to the meta crawler functionality and it obtains the content in the semantic usage data format. Obtained information from the user to the agent will be ranked based on the Enabled Pile clustered data with respect to the metadata information from the agent and end-user. The information is given to the end-user with the top most ranking data within the stipulated time and the remaining top information will be moved to the data repository for future use. The data collected will remain stable based on the user preference and works on the intelligence system approach in which the user can choose any information under any instances and can be provided with suitable high range of exact content. In this approach, we find that the proposed algorithm has produced better results than existing work and it costs less online computation time.展开更多
Traditionally, search engines are designed to support a single user working alone. However, the construction of knowledge is enriched when one adds collaboration to search tasks. We identified opportunities for remote...Traditionally, search engines are designed to support a single user working alone. However, the construction of knowledge is enriched when one adds collaboration to search tasks. We identified opportunities for remote collaboration in a Social Web search model that integrates parents and children guided by 5W + 1H (who, what, where, when, why, how) dimensions. Our social search model aims at improving the search process for children. We found 7 opportunities for remote collaboration on the search process, based on implicit-explicit interactions.展开更多
基金supported by the National Program for Support of Top-notch Young Professionals
文摘Purpose： This article reports on an experiment that tested community members＇ collaborative information seeking （CIS） behavior, with an emphasis on how community type and task difficulty can affect user behavior and user awareness in collaboration.Design/methodology/approach： We carried out a laboratory study with 18 participants in 9 pairs using an experimental CIS system. Data were collected from questionnaires, Web logs and semi-structured interviews. Descriptive statistics and two-way analysis of variance （ANOVA） were used for data analysis. Findings： Compared with non-community members, community participants had a better understanding of search tasks and were aware of the ways of completing tasks successfully. They did not depend on the information retrieval system when constructing search queries and would adopt diversified cooperation strategies. They were more likely to recommend information to their partners. However, no significant difference was found between subject- based community and interest-based community in CIS practices and user awareness in collaboration. In addition, task difficulty only influenced user preference of issuing queries and confidence of completing search tasks. Research limitations： Our work was limited by the community type we chose and the small group size, which could affect the generalizability of our findings and should be addressed in future studies. Practical implications： The study results will help inform information system designers as they design collaborative systems to facilitate social communication in the information seeking process. Originality/value： Few studies have investigated community participants＇ information seeking practices. This study provides insights into community-based CIS behavior. The findings will help us understand social interactions among community members during their information seeking process.
文摘Mining the content from an information database provides challenging solutions to the industry experts and researchers, due to the overcrowded information in huge data. In web searching, the information retrieved is not an appropriate, because it gives ambiguous information for the user query, and the user cannot get relevant information within the stipulated time. To overcome these issues, we propose a new methodology for information retrieval EPCRR by providing the top most exact information to the user, by using the collaborative clustered automated filter which makes use of the collaborative data set and filter works on the prediction by providing the highest ranking for the exact data retrieved. The retrieval works on the basis of recommendation of data which consists of relevant data set with highest priority from the cluster of data which is on high usage. In this work, we make use of the automated wrapper which works similar to the meta crawler functionality and it obtains the content in the semantic usage data format. Obtained information from the user to the agent will be ranked based on the Enabled Pile clustered data with respect to the metadata information from the agent and end-user. The information is given to the end-user with the top most ranking data within the stipulated time and the remaining top information will be moved to the data repository for future use. The data collected will remain stable based on the user preference and works on the intelligence system approach in which the user can choose any information under any instances and can be provided with suitable high range of exact content. In this approach, we find that the proposed algorithm has produced better results than existing work and it costs less online computation time.
文摘Traditionally, search engines are designed to support a single user working alone. However, the construction of knowledge is enriched when one adds collaboration to search tasks. We identified opportunities for remote collaboration in a Social Web search model that integrates parents and children guided by 5W + 1H (who, what, where, when, why, how) dimensions. Our social search model aims at improving the search process for children. We found 7 opportunities for remote collaboration on the search process, based on implicit-explicit interactions.
