| 【Background&Objective】According to the WHO Research Report:1/3human disease can be avoided byhealth care prevention;1/3disease can be effectively controlled through earlydetection; therapeutic effect of1/3disease can be improved through informationcommunication. So, health information resources play an important role on healthcare, disease early intervention and assistant therapy. Internet has become animportant channel to obtain health information resources because of its massive anddiverse information and convenience. Although Internet is not an formal institutionsto get health information resources, it is an important supplement, which can supplyuser information window to in-depth understanding of health care, diseaseprevention and assistant therapy, and enhancement of health consciousness andmanagement. Some researchers find that it is beneficial to health behavior changeand health decision making ability enhancement when users seek health informationonline more often. In recent years, more and more users seek health information onWeb, but because of low health literacy level and Web personalized service, it is verynecessary to further improve the efficiency of seeking information and user satisfaction.Study on the user’s Web information seeking behavior and its characteristicsand regulations can provide users targeted help and improve the networkpersonalized service quality, which is very necessary and meaningful. Tasks are themain factors influencing the information seeking behavior, which is helpful tounderstand why people seek information, and how people select, acquire and useinformation. So, finding out the relationship of tasks and information seekingbehavior is the key point of solving the problem. Universal attention are paid to the influence of task and information seeking behavior, but how to make more targetedpersonalized service accordingly remains to be development. This study intends tofind out the influence relation of task type and information seeking behavior underthe network environment. Based on this, build task type predicting model. Inaddition, characteristics and regulations of information seeking behavior underdifferent task types are analyzed in order to stimulate the corresponding targeted help,which can provide new idea for improving Web personalized service.【Methods】This study adopts the method of experimental research; the data is collectedthrough questionnaires and screen video software.101undergraduate and graduatestudents are invited to participate this experiment.101valid questionnaires and606valid health information seeking task process are obtained.Data are analyzed bystatistical data analysis method. Pridicting model are built by data mining method.【Content&Results】(1) Relationship between Task Characteristics and Network HealthInformation Seeking Behavior①Build Task FrameworkInternal task basic characteristics include objectives, paths and outcomes, andthen extract task complex characteristics on the basic ones such as complexity,structural, etc. It is advantageous for definition and measurement of taskcharacteristics. According to task objectives characteristics in health field, task couldbe divided into three types in this paper, which are fact finding task, informationgathering task and decision making task.External task characteristics emphasis on performer characteristics includingprior knowledge and cognition characteristics, in addition, demographiccharacteristics are also analyzed. Prior knowledge includes health knowledge andnetwork knowledge, which is measured by health information literacy scale.Individual cognitive differences mainly reflected on the cognitive need and cognitivestyle dimension, respectively measured through cognitive need scale and cognitive style figure test. Demographic characteristics including gender, educational level areobtained through the questionnaire. In addition, the effects of website structure andcontent on information seeking behavior are analyzed.②Information Seeking Behavior VariablesInformation seeking behavior variables around seeking process include: queryformulation variables (Time on first query formulation, Query length, Query mode),result list valuation variables (Number of unique SERPs, Number of SERPs, Meanfirst dwell time on unique SERPs, Total dwell time on all SERPs, Mean dwell timeon all SERPs), the targeted site/web page operation variables (Number of savedpages, Number of content pages, Number of antique content pages, Mean first dwelltime on unique content pages, Total dwell time on content pages, Mean dwell timeon content pages, Mean first dwell time on unique content pages/Mean first dwelltime on unique SERPs, Total dwell time on content pages/Total dwell time on allSERPs), operation variables when results dissatisfied (Numbers of queryreformulation, Numbers of search engine or search scope change, Numbers of resultlist recheck), and task overall variables (Task completion time, Website sessions). Inaddition, subjective evaluation method is adopted to assess task performanceincluding degree of effort and satisfaction.③Relationship of Task Characteristics and Information Seeking BehaviorThe statistical analysis results show that:In task internal characteristics (objectives characteristics) aspect, there weresignificant differences among the three task type in regard to behavior variablesexcept inquiry mode and numbers of changing search engines.In task external characteristics aspect, there was significant differences betweenperformer cognition need in regard to total dwell time on SERPs, total dwell time oncontent pages. There were significant differences between performer healthinformation literacy in regard to total number of SERPs, mean first dwell time onunique content pages. There was a significant difference between performereducation degrees in regard to mean dwell time of content pages. There were not significant differences between performer gender and cognition style. In addition,clear site navigation, web links speed, web accessibility, site category had a greaterinfluence on information seeking behavior; The accuracy, authority, accessibility ofthe web content had a greater influence on information seeking behavior.(2) Task Type Prediction Model Research①Whole-session Prediction ModelFeature vector selection: according to the result of task effects on networkinformation seeking behavior variables, select suitable input variables. If there issignificant differences among task types in regard to the information seekingbehavior variable, then choose this variable into the model, in addition, if thevariable is influenced by other factors, then it is regarded as noise, remove it.Through the above analysis,14variables were chosen as follows: Task completiontime, Website sessions, Time on first query formulation, query length, Mean firstdwell time on unique SERPs, Number of unique SERPs, Mean dwell time on allSERPs, Number of saved pages, Number of content pages, Number of antiquecontent pages, Mean first dwell time on unique content pages/Mean first dwell timeon unique SERPs, Total dwell time on content pages/Total dwell time on all SERPs,Numbers of query reformulation, Numbers of result list recheck.The construction method and evaluation of the classifier: Data mining methodwas used to build prediction model with SPSS Clementine based on networkinformation seeking behavior variables. Artificial neural network, decision tree andsupport vector machine method were used to construct classifiers. The results showthat accuracyofartificial neural network model is higher, theaccuracycanreach89.11%.②Within-session Prediction ModelFeature vector selection:5information seeking behavior variables were chosenas follows: Time on first query formulation, Query length, Mean first dwell time onunique SERPs, Mean first dwell time on unique content pages, Mean first dwell timeon unique content pages/Mean first dwell time on unique SERPs. Although theabove5variables was calculated during the whole task session, but these variablescan also be calculated within task session, therefore, these variables can be used aswithin session predictor variables. In addition, according to the former analysis, the variable "Mean first dwell time on unique content pages" affected by performerhealth information literacy, should be removed, but in order to avoid poor modelextensibility because of few input feature vectors, this variable was still included.The construction method and evaluation of the classifier: Data mining methodwas used to build prediction model with SPSS Clementine based on networkinformation seeking behavior variables. Artificial neural network, decision tree andsupport vector machine method were used to construct classifiers. The results showthat accuracy of artificial neural network model is higher, the accuracy can reach78.4%.(3) NetworkPersonalizedServiceStrategyBasedonTaskTypePredictionModel①Perfect Keywords Recommended StrategyA total of420query reformulations under different task type were analyzed, theresults showed that there were differences among task types from queryreformulation type application frequency, situation and effectiveness, according towhich query hints can be provided.②Optimizing Results List Ordering Strategy1025individual queries were divided into742effective query and283ineffective query, and effective query can be predicted via information seekingbehavior variables, the accuracy of artificial neural network model can reach89.52%,which can be applied for further understanding user query intention.【Conclusions】(1)Task has important influence on network health information seekingbehavior.(2)It is feasible for predicting task type based on network health informationseeking behavior and accuracy of pridiction model is high.(3)Web personalized service strategies based on task are very meaningful. |
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