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35 results about "Luser" patented technology

Internet slang prior to the popularization of the Internet in the late 1990s, defined a luser (sometimes expanded to local user; also luzer or luzzer) as a painfully annoying, stupid, or irritating computer user. The word is a blend of "loser" and "user". Among hackers, the word luser takes on a broad meaning, referring to any normal user (in other words, not a "guru"), with the implication the person is also a loser. The term is partially interchangeable with the hacker term lamer.

Method and system for matching user-generated text content

InactiveUS20090132385A1Easily scalable to large databasesMarketingHard codingSelection criterion
According to a computer implemented method and system for matching user-generated text content, users “freely” specify content by means of fed-in texts which are matched automatically, according to rules in the embodiment. An embodiment of the invention allows customers to specify what items or services to request or offer by adding, to the “MyHaves” or “MyWants” selection criteria, using typed-in descriptions. Traditionally, for the purpose of matching supplies and demands, the specification of an individual's “wants” and “haves” is done by selecting options that are predefined by, or hard-coded into, the system's “drop-down menu”—rather than allowing customers to freely define what they want or have. This method under consideration, however, provides an efficacious solution: customers are free to request an item or service by entering standard descriptive texts describing what s/he wants in a customizable manner very akin to the flexibility associated with verbal speech, with the assurance that these human-entered texts will be matched automatically. Similarly, a customer is free to offer an item or service in the aforementioned (text-descriptive) way. The entered texts are in the form of a specific human language (e.g. English, Chinese, etcetera) using the desired input device, such as a computer keyboard. The system algorithm of an implementation then “crawls” through the network of user generated texts (user-defined texts) to find matches between what people are offering and what others are requesting, while watching out for typographical errors (in the text content) made by customers. That is to say, the algorithm in the embodiment scours the texts in the “MyWants” section of requesters and sees if there are corresponding matches found in the “MyHaves” section of offerers, while paying attention to certain system rules.
Although the invention essentially lies in the ability to match raw user-generated texts—that fall out of system-provided categories—to achieve any desired purpose of an embodiment, the invention has applicability in sundry areas where utility may be derived. In an embodiment of the invention, for example, when a match is found, the system automatically triggers an email that is sent to the offerer, notifying him/her that a fellow customer wants what the offerer-customer has to offer. If the offerer-customer agrees to deliver the item or service to the requester, the implementation proceeds to require the requester customer to confirm receipt once the item is received. The utility here is the expeditious re-allocation of resources whose descriptions fall outside the predefined categories of the system and, consequently, may only be accurately provided by the persons wanting or offering the resources (items or services).
Owner:TECHTAIN

Dynamic POIs recommendation method based on TS24

The invention discloses a dynamic POIs (Point of Interest) recommendation method based on TS24, and belongs to the technical field of computer application. The method comprises the following steps: 1, building a dynamic POIs recommendation architecture based on 24 time periods; 2, converting the sign-in frequency of a user into a preference value of the user for the sign-in POIs of the user by using a TF-IDF technology, and constructing a sample set theta cur in a current time period; 3, establishing a SemiDAE geographical influence model on the Tau cur; 4, according to the similarity of the sign-in behaviors of the users in the similar time periods, establishing a T-SemiDAEPOIs recommendation model with the time influence on the Tau cur; and 5, according to the two steps of pre-training and parameter fine tuning, training the T-SemiDAEPOIs recommendation model on the Tau cur. According to the method, the dynamic POIs recommendation model based on the deep learning technology is built in a novel and more reasonable mode, geographical and time information of the location social network is mined and fused, and experimental results show that the applied technology can remarkably improve the precision and recall rate of personalized POIs recommendation.
Owner:DONGBEI UNIVERSITY OF FINANCE AND ECONOMICS
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