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.
E-commerce (using machine learning)
Product finders and recommender systems are used in e-commerce to categorize items and generate recommendations. Online commerce involves large volumes of data and uses systems for data management and analysis. Machine learning and automated tools are used to process large datasets.
Large scale item categorization
Online commerce has expanded over the past decade. Marketplaces such as eBay, Amazon, and Alibaba include millions of items. Item categorization uses tags and labels for product organization. Traditionally, the bag-of-words model has been used in this process, with or without a hierarchy or human-defined structures.
A new method, [4] 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:
- Latent group discovery: used to find groups of classes and the words or features associated to each class. Then we form a confusion matrix between groups to approximate the similarity of classes, the similar classes are kept in a group and so at every stage we get groups with no similarity and hence we get a hierarchy tree.
- At Coarse level we classify the testing instance, for one of the groups at the first level of hierarchy, As the data set is large we cannot use sophisticated algorithm, and thus at this stage either KNN or Naive Bayes is used.
- At fine level we classify the items within a group into some subset group, as there can be similarity in the group we use a sophisticated mechanism, generally SVM at every node.
- KNN (k nearest neighbours) algorithm finds the k neighbours which are really similar to the testing instance, it uses Euclidean or cosine similarity function to find the distance between each class and then gives the top k class.
- electronics → mobile → samsung → case covers. In this example the coarse grained classifier would tell us that the testing instance belongs to electronic group, then we use fine grained at every stage and we got this tree.
The problem faced by these online e-commerce companies are:
- Large Scale,
- Item data extremely sparse
- Skewed distribution over categories
- Heterogeneous characteristics over categories
Recommender system
Recommendation systems are used to recommend consumer items/product based on their purchasing or search history.
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