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In addition, a search engine could provide windowing facilities to allow Web users to generate and track separate topic or related topic queries and facilitate task switching. There is a limited number of studies focusing on topic identification. He, et al. Dempster Shafer Theory enables the combination of two separate probabilistic events related to a single property such as the topic change.

He et al.

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Dempster Shafer Theory requires the probabilities of each event, the weights importance of these separate probabilistic events and a threshold value used to identify a topic shift. Probabilities are easily obtained through the analysis of the data log, as the probability for a specific search pattern and query duration.

To set the weights and the threshold, He, et al. The performance of the topic identification algorithm is determined by the measures of precision and recall, and their combination, the fitness function. Main finding of Ozmutlu and Cavdur was that the application of genetic algorithms for topic identification could be somewhat problematic. Ozmutlu and Cavdur in press. At this point, there is a need for further research, which can enable fast and successful automatic topic identification with a simpler fitness function , and therefore result in effective exploitation of contextual information for development of new search engine algorithms.

In this study, we propose a neural network to automatically identify topic changes. The advantage of neural networks is that the training is done by the actual data, not by the help of a fitness function as in genetic algorithms. The training procedures of the neural network does not depend on any complex performance measure, just the information whether its estimation is correct or not.

In this study, the neural network is trained with a training sample from the datalog and used to identify topic changes on a test dataset. The success of the network is determined comparing its results to that of the human expert. A neural network is an algorithm, which imitates the human brain, in terms of learning a specific concept and functioning with respect to what it has learnt. During the learning process, the input and the output of the problem to be solved are provided to the neural network. Knowing the inputs and the answers, the neural network adapts its synaptic weights.

Then, only the inputs are provided to the neural network and the network provides the answers or output using the values for synaptic weights. Each neural network has neurons or computing cells, which process the information given to the neural network. The data was collected on December 20, and consists of 1,, search queries. In the Excite data log structure, the entries are given in the order they arrive. It is possible to identify new sessions through a user ID and each query contains three fields: 1 Identification: anonymous code assigned by Excite server to a user 2 Tune of day: in hours, minutes, and seconds in US West coast time 3 Query: user terms as entered.

Approximately the first 10, queries of the dataset were selected as a sample from the Excite dataset. The sample size was not kept very large, since evaluation of the performance of the algorithm would require a human expert to go over all the queries. N shift : Number of queries labeled as shifts by the neural network. N contin : Number of queries labeled as continuation by the neural network.

N true shift : Number of queries labeled as shifts by manual examination of human expert.

N truecontin : Number of queries labeled as continuation by manual examination of human expert. Type A error : This type of error occurs in situations where queries on same topics are considered as separate topic groups. Type B error : This type of error occurs in situations where queries on different topics are grouped together into a single topic group.

These performance measures are only used to compare the performance of the proposed neural network to that of the previous studies. The formulation of these values are as follows:. In this study, we propose a neural network to identity topic changes in the Excite search engine query log. The search engine query log consists of 10, search queries. The general steps of the methodology applied in this paper are as follows: Evaluation by human expert.

A human expert goes through the 10, query set and marks the actual topic changes and topic continuations. This step is necessary for training the neural network and also for testing the performance of the neural network. Approximately; first half of the data queries is used to train the data and the second half is used to test the performance of the neural network. Each query in the dataset is categorized in terms of its search pattern and time interval. The time interval is the difference of the arrival times of two consecutive queries.

The classification of the search patterns is based on terms of the consecutive queries within a session. The categorization of time interval and search pattern is selected similar to those of He et al and Ozmutlu and Cavdur in press to avoid any bias during comparison. See Table 1 for distribution of the queries with respect to time interval in the training dataset. It should be noted that not all of queries can be used for training, since the last query of each user session cannot be processed for pattern classification and time duration.

Pattern classification and time duration cannot be determined for the last query of each session, since there are no subsequent queries after the last query of each session. In the training dataset, there were user sessions, so excluding the last query of each session, the test dataset is reduced to queries from queries.

After the human expert identified the topic shifts and continuations, topic continuations and topic shifts were identified within the queries.

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We also use seven categories of search patterns in this study, which are as follows Ozmutlu and Cavdur, in press : Unique New : the second query has no common term compared to the first query. Generalization: all of the terms of second query are also included in the first query but the first query has some additional terms. Specialization: all of the terms of the first query are also included in the second query but the second query has some additional terms.

In , it bought a Colorado company called MatchLogic that promised to transmute eyeballs into dollars - tracking users, targeting them demographically, delivering the kind of specifics to advertisers that print and broadcasting can only dream of. The MatchLogic people also contributed a slogan that captured Excite's way of thinking: "Go big or stay home. Bell was a charismatic leader, an Emmy-winning television producer whose exploits shooting documentaries in the Amazon and the Himalayas gave him a certain Indiana Jones mystique.

Making Excite a contender had been quite a feat, but it was becoming clear that there were too many portals for them all to survive. Consolidation was imperative. Excite didn't necessarily disdain the media giants, former executives maintain, but it also didn't see a marriage of portal and content as the ideal combo. Bell was growing suspicious of the everything-for-free motto of the Web. Paid subscriptions and services seemed more the way to go. When AOL made a deal that fall to buy Netscape, the heat was on.

By the beginning of , Excite had two deals on the table and another that seemed close. Worse, selling to Yahoo! Microsoft was interested in merging Excite with the Microsoft Network, which would put it in the paid access business; unfortunately, Microsoft president Steve Ballmer seemed in no hurry to make up his mind. That left Home, the cable modem service, headquartered across the street in Redwood City. In exchange for a five-year exclusive, Home would offer high-speed Internet access via cable modem to TCI customers.

This looked like a good deal to Malone, and to Cox and Comcast and the other cable operators that later joined in. Home provided them with the network capacity, the sales and marketing effort, and the billing and service operations needed to offer broadband, and gave them the bulk of the subscriber revenue as well.

The cable companies still faced huge expenses in upgrading their lines to handle two-way data traffic - but at least with Home they got holdings in a high-flying Internet stock.

How to Open an Excite Email Account

It might have worked a lot better if they'd structured the partnership to benefit Home and the cable operators equally. As it was, the cable guys were at odds because they each received equity in Home in proportion to the total number of residences in their service area - even though TCI, which reached the most homes, had such low tech cable systems that most of those homes weren't ready for Internet access.

Because the cable operators owned most of the company, the board was dominated by people whose first loyalty, arguably, was not to Home but to their own employers. The cable operators and Home had no incentive to work together on advertising, because all national ad revenue went to Home while all local ad money went to the cable companies.

And because no one paid enough attention to customer service, subscribers who phoned in with complaints got shuttled back and forth between the cable call centers, which were ill-equipped to deal with modem questions, and the Home call center, which was ill-equipped to deal with customers. The ultimate issue, however, was self-image. Home viewed itself as a tech venture in the tradition of Intel, Apple, and Sun.

Under the circumstances, Home would have done well to address its own problems. But its CEO, Tom Jermoluk, figured that with Excite's help, he could develop video content to boost the wow factor and stimulate demand. He'd already negotiated a deal with Lycos, but Excite was his first choice. As for Excite, George Bell had just been forced to scrap plans to expand ecommerce and launch the first new marketing campaign in years, because only by slashing costs could he make good on his promise to show a profit by fall.

By putting Excite's users together with Home's subscribers actually they were the cable companies' subscribers, but that technicality tended to be overlooked , they could create the AOL of broadband.

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Hindery promised equal treatment for all content providers. Jermoluk, livid, blocked the deals. But the issue had already exploded.

click They were in a better position than most to suspect that this pairing could turn out badly. Apparently, however, that thought never surfaced. A well-received merger agreement, with Home paying a hefty premium to buy Excite, would allow Kleiner Perkins to distribute the remainder of its shares to its venture-fund investors while both stocks were riding high.