Recommendation Algorithms for E-Commerce

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Recommendation Algorithms for E-Commerce
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Introduction

• Millions of products are sold over the web.
  Choosing among so many options is proving
  challenging for consumers. Recommender systems
  have emerged has a response to this problem.

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Collaborative filtering(Cf)

• Works by building a database of preferences for
  products by customers. A new customer is matched
  to discover neighbors, who have the same
  tastes. Then the products that these customers
  bought are recommended to the new users.

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Problems with Cf

• The two conflicting issues related to this
  technique are scalability and accuracy.
• While these techniques are good enough for
  neighbor discovery in databases where the number
  of customers is in some thousands, they however
  fail, when the sizes reach to the order of
  millions. Also as the size of each record
  increases with more data points to be considered,
  the problem is increased.

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Problems with Cf continued

• Another issue is of accuracy. In the context of
  predictions we have 2 error conditions, one a
  false positive, where the system recommends a
  product the user eventually doesnt like, and a
  false negative, where the system assumes that a
  product will not be liked by a user when it not
  so in reality. It is much more dangerous to have
  a false positive, because that will lead angry
  customers!

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Problems with Cf continued

• These two issues are conflicting in the sense
  that in order to be fast a recommender system may
  not search exhaustively through the database and
  thus increase the chances for an error.

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Recommender Systems based on Cf

• There are three phases of operation
• Representation In a typical CF-based recommender
  system, the input data is a collection of
  historical purchasing transactions of n customers
  on m products. It is usually represented as an
  mxn customer-product matrix, R, such that rij is
  one if the ith customer has purchased the jth
  product, and zero, otherwise. We term this m n
  representation of the input data set as original
  representation.

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Recommender Systems based on Cf

• Neighborhood formationThe most important step in
  CF-based recommender systems is that of computing
  the similarity between customers as it is used to
  form a proximity based neighborhood between a
  target customer and a number of like-minded
  customers. The neighborhood formation process is
  in fact the model-building or learning process
  for a recommender system algorithm.
• The third step is recommendation generation