Application of automatic topic identification on Excite Web search engine data logs

Özmutlu, H. Cenk; Çavdur, Fatih

doi:10.1016/j.ipm.2004.04.018

Cited by 61 publications

(84 citation statements)

References 21 publications

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“…Some previous works propose exploiting the lexical content of queries in order to determine session boundaries corresponding to possible topic shifts in the stream of queries issued by users [Lau and Horvitz 1999;He et al 2002;Ozmutlu and Ç avdur 2005]. To this end, several search patterns are proposed by means of lexical comparison based on different string similarity metrics (e.g., Levenshtein, Jaccard, etc.…”

Section: Related Workmentioning

confidence: 99%

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Discovering tasks from search engine query logs

Lucchese

Orlando

Perego

et al. 2013

ACM Trans. Inf. Syst.

102

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Although Web search engines still answer user queries with lists of ten blue links to webpages, people are increasingly issuing queries to accomplish their daily tasks (e.g., finding a recipe, booking a flight, reading online news, etc.). In this work, we propose a two-step methodology for discovering tasks that users try to perform through search engines. First, we identify user tasks from individual user sessions stored in search engine query logs. In our vision, a user task is a set of possibly noncontiguous queries (within a user search session), which refer to the same need. Second, we discover collective tasks by aggregating similar user tasks, possibly performed by distinct users. To discover user tasks, we propose query similarity functions based on unsupervised and supervised learning approaches. We present a set of query clustering methods that exploit these functions in order to detect user tasks. All the proposed solutions were evaluated on a manually-built ground truth, and two of them performed better than state-of-the-art approaches. To detect collective tasks, we propose four methods that cluster previously discovered user tasks, which in turn are represented by the bag-of-words extracted from their composing queries. These solutions were also evaluated on another manually-built ground truth.

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Section: Related Workmentioning

confidence: 99%

“…Similarly, Ozmutlu and Ç avdur [2005] describe a mechanism for identifying topic changes in user search behavior by combining time and query content features. They test the validity of their approach using a genetic algorithm in order to learn the parameters of the topic identification task.…”

Section: Related Workmentioning

confidence: 99%

Discovering tasks from search engine query logs

Lucchese

Orlando

Perego

et al. 2013

ACM Trans. Inf. Syst.

102

View full text Add to dashboard Cite

show abstract

“…He et al (2002) used the search pattern and duration of a query for new topic identification. Their approach was replicated on Excite search engine data (Ozmutlu and Cavdur, 2005a). Ozmutlu and Cavdur (2005b) and Ozmutlu, et al (2004a) proposed an artificial neural network to automatically identify topic changes, and showed that neural networks successfully provided new topic identification.…”

Section: Introduction and Related Researchmentioning

confidence: 99%

Investigating the performance of automatic new topic identification across multiple datasets

Özmutlu

Çavdur

Spink

et al. 2006

Proc of Assoc for Info

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“…It describes WEBMINER, system for Web usage mining, and comprises the paper by categorizing various research issues. Mining typical user profiles [10] and URL associations from the huge amount of access logs is an important component of Web personalization. In this paper this defines the notion of a user session as being a temporally compact sequence of Web accesses by a user.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Preceding studies on this recommender system have mainly focused on modeling techniques and feature development, this content optimization is based on general behavior analysis algorithm. This argues that suitable user action analysis is critical for a recommender system [7]. Our system proposes a novel user feedback and event monitoring schemes for effective content optimization technique.…”

Section: Introductionmentioning

confidence: 99%

Online content optimization with effective event monitoring approach

Sajithra.¹,

Priyadarshini²

2015

International Journal of Advanced Research in Computer and Comm

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Content optimization has widely used in personalized search engines for better personalized results. It can be generated by the user actions and events on the web search engine. User interaction on the page plays a vital role in recommender systems. Previous studies on recommender systems mainly focused on modeling techniques and feature development, this content optimization is based on general behavior analysis algorithm. It provides user action analysis is critical for a recommender system. The system proposes a novel implicit user feedback and event monitoring schemes for efficient content optimization. For this our system proposes PCO approach. But user interactions in realworld Web applications are unlikely to be as ideal as those assumed by previously proposed models. Our proposed system builds an online dynamic learning framework for personalized recommendation. The main contribution in this paper is an approach of personalizing users' searches to achieve better search result which is based on event monitoring and personalized content optimized search.

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Application of automatic topic identification on Excite Web search engine data logs

Cited by 61 publications

References 21 publications

Discovering tasks from search engine query logs

Discovering tasks from search engine query logs

Investigating the performance of automatic new topic identification across multiple datasets

Online content optimization with effective event monitoring approach

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