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- 1104 creator damon-horowitz.
- 1104 creator sepandar-kamvar.
- 1104 type InProceedings.
- 1104 label "The Anatomy of a Large-Scale Social Search Engine".
- 1104 sameAs 1104.
- 1104 abstract "We present Aardvark, a social search engine. With Aardvark, users ask a question, either by instant message, email, web input, text message, or voice. Aardvark then routes the question to the person in the user's extended social network most likely to be able to answer that question. As compared to a traditional web search engine, where the challenge lies in finding the right {\it document} to satisfy a user's information need, the challenge in a social search engine like Aardvark lies in finding the right {\it person} to satisfy a user's information need. Further, while trust in a traditional search engine is based on authority, in a social search engine like Aardvark, trust is based on intimacy. We describe how these considerations inform the architecture, algorithms, and user interface of Aardvark, and how they are reflected in the behavior of Aardvark users.".
- 1104 hasAuthorList authorList.
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- 1104 keyword "Use of social metadata".
- 1104 keyword "annotations in search".
- 1104 keyword "mining".
- 1104 title "The Anatomy of a Large-Scale Social Search Engine".
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- item-1 hasContent damon-horowitz.
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- 119 creator bin-gao.
- 119 creator tie-yan-liu.
- 119 creator zhicong-cheng.
- 119 type InProceedings.
- 119 label "Actively Predicting Diverse Search Intent from User Browsing Behaviors".
- 119 sameAs 119.
- 119 abstract "This paper is concerned with actively predicting search intent from user browsing behavior data. In recent years, great attention has been paid to predicting user search intent. However, the prediction was mostly passive because it was performed only after users submitted their queries to search engines. It is not considered why users issued these queries, and what triggered their information needs. According to our study, many information needs of users were actually triggered by what they have browsed. That is, after reading a page, if a user found something interesting or unclear, he/she might have the intent to obtain further information and accordingly formulate a search query. Actively predicting such search intent can benefit both search engines and their users. In this paper, we propose a series of technologies to fulfill this task. First, we extract all the queries that users issued after reading a given page from user browsing behavior data. Second, we learn a model to effectively rank these queries according to their likelihoods of being triggered by the page. Third, since search intents can be quite diverse even if triggered by the same page, we propose an optimization algorithm to diversify the ranked list of queries obtained in the second step, and then suggest the list to users. We have tested our approach on large-scale user browsing behavior data obtained from a commercial search engine. The experimental results have shown that our approach can predict meaningful queries for a given page, and the search performance for these queries can be significantly improved by using the triggering page as contextual information.".
- 119 hasAuthorList authorList.
- 119 isPartOf proceedings.
- 119 keyword "Query log".
- 119 keyword "click trail".
- 119 keyword "traffic data mining".
- 119 title "Actively Predicting Diverse Search Intent from User Browsing Behaviors".
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- item-1 hasContent zhicong-cheng.
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- 161 creator dimitrios-skoutas.
- 161 creator mohammad-alrifai.
- 161 creator thomas-risse.
- 161 type InProceedings.
- 161 label "Selecting Skyline Services for QoS-based Web Service Composition".
- 161 sameAs 161.
- 161 abstract "Web service composition enables seamless and dynamic integration of business applications on the web. The performance of the composed application is determined by the performance of the involved web services. Therefore, non-functional, quality of service (QoS) aspects (e.g. response time, availability, etc.) are crucial for selecting the web services to take part in the composition. The problem of identifying the best candidate web services from a set of functionally-equivalent services is a multi-criteria decision making problem. The selected services should optimize the overall QoS of the composed application, while satisfying all the constraints specified by the client on individual QoS parameters. In this paper, we propose an approach based on the notion of skyline to effectively and efficiently select services for composition, reducing the number of candidate services to be considered. We also discuss how a provider can improve its service to become more competitive and increase its potential of being included in composite applications. We evaluate our approach experimentally using both real and synthetically generated data sets.".
- 161 hasAuthorList authorList.
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- 161 keyword "Quality of service".
- 161 keyword "service level agreements (SLAs)".
- 161 title "Selecting Skyline Services for QoS-based Web Service Composition".
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- item-1 hasContent mohammad-alrifai.
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- item-2 hasContent dimitrios-skoutas.
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- 172 creator guy-lebanon.
- 172 creator kevyn-collins-thompson.
- 172 creator mingxuan-sun.
- 172 type InProceedings.
- 172 label "Visualizing Differences in Web Search Algorithms using the Expected Weighted Hoeffding Distance".
- 172 sameAs 172.
- 172 abstract "We introduce a new dissimilarity function for ranked lists, the expected weighted Hoeffding distance, that has several advantages over current dissimilarity measures for ranked search results. First, it is easily customized for users who pay varying degrees of attention to websites at different ranks. Second, unlike existing measures such as generalized Kendall's tau, it is based on a true metric, preserving meaningful embeddings when visualization techniques like multi-dimensional scaling are applied. Third, our measure can effectively handle partial or missing rank information while retaining a probabilistic interpretation. Finally, the measure can be made computationally tractable and we give a highly efficient algorithm for computing it. We then apply our new metric with multi-dimensional scaling to visualize and explore relationships between the result sets from different search engines, showing how the weighted Hoeffding distance can distinguish important differences in search engine behavior that are not apparent with other rank-distance metrics. Such visualizations are highly effective at summarizing and analyzing insights on which search engines to use, what search strategies users can employ, and how search results evolve over time. We demonstrate our techniques using a collection of popular search engines, a representative set of queries, and frequently used query manipulation methods.".
- 172 hasAuthorList authorList.
- 172 isPartOf proceedings.
- 172 keyword "Data exploration".
- 172 keyword "visualization".
- 172 title "Visualizing Differences in Web Search Algorithms using the Expected Weighted Hoeffding Distance".
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