The fact that the World Wide Web is being used for various purposes also implies that users may have various information quality factors to consider according to their current context. In this regards, it is important for Web recommendation services to recognize what quality factors should be considered in current context in order to enhance user satisfaction. We showed that it is necessary to classify Web contexts based on the information quality factors users consider in their minds when they choose websites or Web pages. The results of user interviews showed that there are four quality factors: credibility, recency, popularity, and relevance. From survey data analysis, we recognized that user tasks can be clustered into two groups based on the quality factors that users consider. Finally, the results of log data analysis and performances of our proposed algorithm showed that it is possible to enable Web services to infer the context group. This result implies that context recognition is possible using the limited data that are collected at browser side.