Accuracy of Bilingual Chinese-speakers using search systems

Internet users have substantial trust in search engine’s ability to rank the re-sults by the relevance to the query. This paper is seeking to understand how three factors affect the accuracy for native bilingual Chinese-speaking inter-net users. The factors are proficiency of English, the position of relevant in-formation on search engine result page (SERP) and system language. Sub-jects in this research interacted with simulated search engines and they were asked to identify the best results on SERP. The results show that the system language or English ability alone do not affect subjects’ performance, only if those two factors work together effect on subjects’ performance on finding results on SERP. Rank basis exists in bilingual Chinese-speakers and they tent to click on the results on higher locations. Target location in different system language only matters in the group of subjects within average Eng-lish ability, not in the group of low or high English ability subjects. But the accuracy performances are reversed in low English ability compare to high English ability

Examining repetition in user search behavior

This paper describes analyses of the repeated use of search engines. It is shown that users commonly re-issue queries, either to examine search results deeply or simply to query again, often days or weeks later. Hourly and weekly periodicities in behavior are observed for both queries and clicks. Navigational queries were found to be repeated differently from others

Cross Validation Of Neural Network Applications For Automatic New Topic Identification

There are recent studies in the literature on automatic topic-shift identification in Web search engine user sessions; however most of this work applied their topic-shift identification algorithms on data logs from a single search engine. The purpose of this study is to provide the cross-validation of an artificial neural network application to automatically identify topic changes in a web search engine user session by using data logs of different search engines for training and testing the neural network. Sample data logs from the Norwegian search engine FAST (currently owned by Overture) and Excite are used in this study. Findings of this study suggest that it could be possible to identify topic shifts and continuations successfully on a particular search engine user session using neural networks that are trained on a different search engine data log

On the interplay between search behavior and collections in digital libraries and archives

Log analysis is an unobtrusive technique used to better understand search behavior and evaluate search systems. However, in contrast with open web search, in a vertical search system such as a digital library or media archive the collection is known and central to its purpose. This drives different, more collection-oriented questions when studying the logs. For example, whether users need different support in different parts of the collection.In a digital library, the collection is categorized using professionally curated metadata. We conjecture that using this metadata can improve and extend the methods and techniques for log analysis. We investigate how to identify different types of search behavior using the metadata explicitly, how to explain and predict user interactions for the different types of behavior found, and finally how to communicate our research results to domain experts.</p

Performance comparison of clustered and replicated information retrieval systems

The amount of information available over the Internet is increasing daily as well as the importance and magnitude of Web search engines. Systems based on a single centralised index present several problems (such as lack of scalability), which lead to the use of distributed information retrieval systems to effectively search for and locate the required information. A distributed retrieval system can be clustered and/or replicated. In this paper, using simulations, we present a detailed performance analysis, both in terms of throughput and response time, of a clustered system compared to a replicated system. In addition, we consider the effect of changes in the query topics over time. We show that the performance obtained for a clustered system does not improve the performance obtained by the best replicated system. Indeed, the main advantage of a clustered system is the reduction of network traffic. However, the use of a switched network eliminates the bottleneck in the network, markedly improving the performance of the replicated systems. Moreover, we illustrate the negative performance effect of the changes over time in the query topics when a distributed clustered system is used. On the contrary, the performance of a distributed replicated system is query independent

Demographic information flows

ABSTRACT In advertising and content relevancy prediction it is important to understand whether, over time, information that reaches one demographic group spreads to others. In this paper we analyze the query log of a large U.S. web search engine to determine whether the same queries are performed by different demographic groups at different times, particularly when there are query bursts. We obtain aggregate demographic features from user-provided registration information (gender, birth year, ZIP code), U.S. census data, and election results. Given certain queries, we examine trends (from high to low and vice versa) and changes in the statistical spread of the demographic features of users that issue the queries over time periods that include query bursts. Our analysis shows that for certain types of queries (movies and news) distinct demographic groups perform searches at different times, suggesting that information related to such queries flows between them. Queries of movie titles, for instance, tend to be issued first by young and then by older users, where a sudden jump in age occurs upon the movie&apos;s release. To the best of our knowledge, this is the first time this problem has been studied using search query logs

Investigating Per-user Time Sensitivity Of Search Topics

Search engines give the same results for the same query. They do not consider that a user’s topics of interest may diverge at different times even if the query terms are the same. This paper presents the findings of a study into how different topics of interest of a user are influenced by time. The results show that most of the users have time sensitive search patterns, indicating that they have different topics of interest that are dominant at different times