H. Cenk Ozmutlu

Multitasking information seeking and searching processes

Recent studies show that humans engage in multitasking behaviors as they seek and search information retrieval (IR) systems for information on more than one topic at the same time. For example, a Web search session by a single user may consist of searching on single topics or multitasking. Findings are presented from four separate studies of the prevalence of multitasking information seeking and searching by Web, IR system, and library users. Incidence of multitasking identified in the four different studies included: (1) users of the Excite Web search engine who completed a survey form, (2) Excite Web search engine users filtered from an Excite transaction log from 20 December 1999, (3) mediated on-line databases searches, and (4) academic library users. Findings include: (1) multitasking information seeking and searching is a common human behavior, (2) users may conduct information seeking and searching on related or unrelated topics, (3) Web or IR multitasking search sessions are longer than single topic sessions, (4) mean number of topics per Web search ranged of 1 to more than 10 topics with a mean of 2.11 topic changes per search session, and (4) many Web search topic changes were from hobbies to shopping and vice versa. A more complex model of human seeking and searching levels that incorporates multitasking information behaviors is presented, and a theoretical framework for human information coordinating behavior (HICB) is proposed. Multitasking information seeking and searching is developing as major research area that draws together IR and information seeking studies toward a focus on IR within the context of human information behavior. Implications for models of information seeking and searching, IR/Web systems design, and further research are discussed.

Web searching for sexual information: an exploratory study

Sexuality on the Internet takes many forms and channels, including chat rooms discussions, accessing Websites or searching Web search engines for sexual materials. The study of Web sexual queries provides insight into sexual-related information-seeking behavior, of value to Web users and providers alike. We qualitatively analyzed 58,027 queries from a log of 1,025,910 Excite Web user queries from 1999. We found that sexual and non-sexual-related queries exhibited differences in session duration, query outcomes, and search term choices. Implications for sexual information seeking and Web systems are discussed.

Neural network applications for automatic new topic identification on excite web search engine data logs

The analysis of contextual information in search engine query logs is an important, yet difficult task. Users submit few queries, and search multiple topics sometimes with closely related context. Identification of topic changes within a search session is an important branch of contextual information analysis. The purpose of this study is to propose a topic identification algorithm using neural networks. A sample from the Excite data log is selected to train the neural network and then the neural network is used to identify topic changes in the data log. As a result, 76% of topic shifts and 92% of topic continuations are identified correctly.

Genetic algorithm with local search for the unrelated parallel machine scheduling problem with sequence-dependent set-up times

In this paper, a genetic algorithm (GA) with local search is proposed for the unrelated parallel machine scheduling problem with the objective of minimising the maximum completion time (makespan). We propose a simple chromosome structure consisting of random key numbers in a hybrid genetic-local search algorithm. Random key numbers are frequently used in GAs but create additional difficulties when hybrid factors are implemented in a local search. The best chromosome of each generation is improved using a local search during the algorithm, but the better job sequence (which might appear during the local search operation) must be adapted to the chromosome that will be used in each successive generation. Determining the genes (and the data in the genes) that would be exchanged is the challenge of using random numbers. We have developed an algorithm that satisfies the adaptation of local search results into the GAs with a minimum relocation operation of the genes’ random key numbers – this is the main contribution of the paper. A new hybrid approach is tested on a set of problems taken from the literature, and the computational results validate the effectiveness of the proposed algorithm.

Automatic new topic identification in search engine transaction logs

Purpose – Content analysis of search engine user queries is an important task, since successful exploitation of the content of queries can result in the design of efficient information retrieval algorithms of search engines, which can offer custom‐tailored services to the web user. Identification of topic changes within a user search session is a key issue in content analysis of search engine user queries. The purpose of this study is to address these issues.Design/methodology/approach – This study applies genetic algorithms and Dempster‐Shafer theory, proposed by He et al., to automatically identify topic changes in a user session by using statistical characteristics of queries, such as time intervals and query reformulation patterns. A sample data log from the Norwegian search engine FAST (currently owned by overture) is selected to apply Dempster‐Shafer theory and genetic algorithms for identifying topic changes in the data log.Findings – As a result, 97.7 percent of topic shifts and 87.2 percent of to...

Multimedia Web Searching Trends.

In this paper we examine and compare multimedia Web searching by Excite and FAST search engine users in 2001. Findings include: (1) some differences between the Excite and FAST multimedia Web searches, (2) FAST users submit more multimedia queries but fewer audio-related queries, (3) FAST users take more time with queries and sessions compared to Excite users, but spend less time on audio queries, and (4) Excite users submit longer and more complex queries than FAST users. As the Excite search engine is U.S. based and FAST search engine is Norwegian based, these differences suggest some difference in the Web search behavior of U.S. and European Web users.

Managing End-to-End Network Performance via Optimized Monitoring Strategies

To predict the delay between a source and a destination as well as to identify anomalies in a network, it is useful to continuously monitor the network by sending probes between all sources and destinations. Some of the problems of such probing strategies are: (1) there is a very large amount of information to analyze in real time; and (2) the probes themselves could add to the congestion. Therefore it is of prime importance to reduce the number of probes drastically and yet be able to reasonably predict delays and identify anomalies. In this paper we formulate a graph-theoretic problem called the Constrained Coverage Problem to optimally select a subset to traceroute-type probes to monitor networks where the topology is known. To solve this problem, we develop a heuristic algorithm called the Constrained Coverage Heuristic (CCH) algorithm, which works in polynomial time, as an alternative to the standard exponential-time integer programming solution available in commercial software. The application of the Constrained Coverage Problem to randomly generated topologies yielded an 88.1% reduction in the number of monitored traceroute-type probes on average. In other words, networks can be successfully monitored using only 11.9% of all possible probes. For these examples, the polynomial time CCH algorithm performed remarkably well in comparison to the standard exponential time integer programming algorithm and obtained the optimal (in 24 of 30 examples) or near optimal (second best solution in the remaining examples) solutions in a comparatively negligible amount of time.

Automatic New Topic Identification in Search Engine Transaction Logs Using Multiple Linear Regression

Content analysis of search engine user queries is an important task for search engine research, and identification of topic changes within a user search session is a key issue in content analysis of search engine user queries. The purpose of this study is to provide automatic new topic identification of search engine query logs, and estimate the effect of statistical characteristics of search engine queries on new topic identification. By applying multiple linear regression and ANOVA on a sample data log from the FAST search engine, we have reached the following findings: 1) We demonstrated that the statistical characteristics of Web search queries are effective on shifting to a new topic; 2) Multiple linear regression is a successful tool for estimating topic shifts and continuations. This study provides statistical proof for the relationship between the non-semantic characteristics of Web search queries and the occurrence of topic shifts and continuations.

Introducing the MCHF/OVRP/SDMP: Multicapacitated/Heterogeneous Fleet/Open Vehicle Routing Problems with Split Deliveries and Multiproducts

In this paper, we analyze a real-world OVRP problem for a production company. Considering real-world constrains, we classify our problem as multicapacitated/heterogeneous fleet/open vehicle routing problem with split deliveries and multiproduct (MCHF/OVRP/SDMP) which is a novel classification of an OVRP. We have developed a mixed integer programming (MIP) model for the problem and generated test problems in different size (10–90 customers) considering real-world parameters. Although MIP is able to find optimal solutions of small size (10 customers) problems, when the number of customers increases, the problem gets harder to solve, and thus MIP could not find optimal solutions for problems that contain more than 10 customers. Moreover, MIP fails to find any feasible solution of large-scale problems (50–90 customers) within time limits (7200 seconds). Therefore, we have developed a genetic algorithm (GA) based solution approach for large-scale problems. The experimental results show that the GA based approach reaches successful solutions with 9.66% gap in 392.8 s on average instead of 7200 s for the problems that contain 10–50 customers. For large-scale problems (50–90 customers), GA reaches feasible solutions of problems within time limits. In conclusion, for the real-world applications, GA is preferable rather than MIP to reach feasible solutions in short time periods.

Investigating the Performance of Automatic New Topic Identification Across Multiple Datasets

Recent studies on automatic new topic identification in Web search engine user sessions demonstrated that neural networks are successful in automatic new topic identification. However most of this work applied their new topic identification algorithms on data logs from a single search engine. In this study, we investigate whether the application of neural networks for automatic new topic identification are more successful on some search engines than others. 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 query logs with more topic shifts tend to provide more successful results on shift-based performance measures, whereas logs with more topic continuations tend to provide better results on continuation-based performance measures.