Product finder was patented in London in December 1818, becoming Britain's first bicycle. It featured an elegantly curved wooden frame, allowing the use of larger wooden wheels. Several parts were made of metal, which allowed the vehicle to be lighter than the continental version. Although Johnson referred to his machine as a ‘pedestrian curricle’, it was formally referred to as a ‘velocipede’, and popularly as a ‘Hobby-horse’, ‘Dandy-horse’, ‘Pedestrian's accelerator’, ‘Swift walker’ and by a variety of other names. Johnson made at least 320 velocipedes in the early part of 1819. He also opened riding schools in the 	Denis Johnson (inventor)
5529621	Strand and Soho. In May 1819 he introduced a dropped-frame version for ladies to accommodate their long skirts. For about six months the machine had a high profile in London and elsewhere, its principal riders being the Regency dandies. About eighty prints were produced in London, depicting the 'hobby-horse' and its users, not always in a flattering light. Johnson undertook a tour of England in the spring of 1819 to exhibit and publicise the item. Nevertheless, by the summer of the same year the craze was dying out, and a health warning against the continued use of the 	Denis Johnson (inventor)
5529622	velocipede was issued by the London Surgeons. In Johnson's machine, like that of von Drais, propulsion was simply by ‘swift walking’, with the rider striking his (or her) feet on the ground alternately. However, it led directly (albeit after a long delay) to the invention of the bicycle in the 1860s, when rotary cranks and pedals were attached to the front-wheel hub of a machine based on Johnson's. Legacy. A memorial plaque at 69–75 Long Acre, Covent Garden, the former site of his workshop, was unveiled on 2 July 1998 by Tony Banks MP, Minister for Sport	Denis Johnson (inventor)
5529623	Product finder Product finder Product finders are information systems that help consumers to identify products within a large palette of similar alternative products. Product finders differ in complexity, the more complex among them being a special case of decision support systems. Conventional decision support systems, however, aim at specialized user groups, e.g. marketing managers, whereas product finders focus on consumers. Area of application. Usually, product finders are part of an e-shop or an online presentation of a product-line. Being part of an e-shop, a product finder ideally leads to an online buy, while conventional distribution channels are involved in 	Product finder
5529624	product finders that are part of an online presentation (e.g. shops, order by phone). Product finders are best suited for product groups whose individual products are comparable by specific criteria. This is true, in most cases, with technical products such as notebooks: their features (e.g. clock rate, size of harddisk, price, screen size) may influence the consumer's decision. Beside technical products such as notebooks, cars, dish washers, cell phones or GPS devices, non-technical products such as wine, socks, toothbrushes or nails may be supported by product finders as well, as comparison by features takes place. On the other 	Product finder
5529625	hand, the application of product finders is limited when it comes to individualized products such as books, jewelry or compact discs as consumers do not select such products along specific, comparable features. Furthermore, product finders are used not only for products sensu stricto, but for services as well, e.g. account types of a bank, health insurance, or communication providers. In these cases, the term service finder" is used sometimes. Product finders are used both by manufacturers, dealers (comprising several manufacturers), and web portals (comprising several dealers). There is a move to integrate Product finders with social networking and group buying  allowing users to add and rate products, locations and purchase recommended products with others. Technical implementation. Technical implementations differ in their benefit for the consumers. The following list displays the main approaches, from simple ones to more complex ones, each with a typical example: E-commerce (using machine learning). Product finder has an important role in e-commerce, items has to be categorized to better serve consumer in searching the desired product, recommender system for recommending items based on their purchases etc. As people are moving from offline to online commerce (e-commerce), it is getting more difficult and cumbersome  to deal with the large amount of data about items, people that need to be kept and analyzed in order to better serve consumer. Large amount of data cannot be handled by just using man power, we need machine to do these things for us, they can deal with large amount of data efficiently and effectively. Large scale item categorization. Online commerce has gained a lot of popularity over the past decade. Large online consumer to consumer marketplaces such as eBay, Amazon, Alibaba feature a very large almost millions of items entered into the marketplace every day. Item categorization  helps in classifying product and giving them tags and label which helps consumer to find it. Traditionally bag-of-words model approach is used to solve the problem with using no hierarchy at all or using human-defined hierarchy. But, a new method, using hierarchical approach which decomposes the classification problem into a coarse level task and a fine level task, with the hierarchy made using latent class model discovery. A simple classifier is applied to perform the coarse level classification (because the data is so large we cannot use more sophisticated approach due to time issue) while a  more sophisticated model is used to separate classes at the fine level. Highlights/Methods used: The problem faced by these online e-commerce companies are: Recommender system. Recommendation systems are used to recommend consumer items/product based on their purchasing or search history. Product finder Product finders are information systems that help consumers to identify products within a large palette of similar alternative products. Product finders differ in complexity, the more complex among them being a special case of decision support systems. Conventional decision support systems, however, aim at specialized user groups, e.g. marketing managers, whereas product finders focus on