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 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 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, and Alibaba feature millions of items with more entered into the marketplace every day. Item categorization helps in classifying products and giving them tags and labels, which helps consumer find them. Traditionally bag-of-words model approach is used to solve the problem with using no hierarchy at all or using human-defined hierarchy. 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.