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Discovering Association Rules for Recommendation

This paper explores strategies for addressing the recommendation problem in library systems, particularly in the context of the NSYSU library, which faces unique challenges due to new users and new book acquisitions. It discusses two main approaches: content-based and collaborative filtering, each with its advantages and disadvantages. The study focuses on profile association rule discovery using data from the library's transaction database to identify relevant user interests and optimize the recommendation of newly added books. The goal is to enhance user experience while managing advertising costs effectively.

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Discovering Association Rules for Recommendation

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  1. Discovering Association Rules for Recommendation 2004/10/07

  2. Outline • Recommendation problem • Content-based approach • Collaborative approach • Profile association rules discovery • Rule discovery • Evaluation

  3. Recommendation problem • The effectiveness of targeting a small, selected portion of customers for advertising has long been recognized by businesses. • Avoid information overload • Reduce cost • Ex. Amazon, UDN, Citeseer, etc.

  4. Recommendation problem • Content-based approach • Recommendable products are characterized by a set of content features. • Customer’s interest is represented by the same feature set. • Target customers whose interests have a high degree of similarity with the product are selected.

  5. Recommendation problem • Collaborative approach • Looks for relevance among users by observing the ratings • The nearest-neighbor users are those that exhibit the strongest relevance to the target user. • Products that appear in the profiles of partners are recommended to the target user.

  6. Recommendation problem • Disadvantages of content-based approach • Inability to advertise products to a new user. • Inability to provide novel advertisements. • Disadvantages of collaborative approach • Inability to advertise products to a new user. • Inability to advertise new products. • Poor prediction performance when data are sparse.

  7. Profile association rule discovery • The new book recommendation problem in NSYSU library: • NSYSU has approximately 7000 enrolled students • Its library approximately buys 1000 new books per month • One of the library’s e-services is promoting new books to NSYSU’s students (via email).

  8. Profile association rule discovery • The new-book feature makes the collaborative approach inapplicable. • The new-user feature makes the content-based approach inapplicable. • The library uses an online public access catalog (OPAC) system. Huge amount of data has been collected in their database.

  9. Profile association rule discovery • Patron hierarchy • Book hierarchy • ISBN standard

  10. Profile association rule discovery • Transaction database (OPAC log) • Translated transactions

  11. Profile association rule discovery • The association rule algorithm is applied on the transaction data. • Rules whose right hand side are not book types are removed. • For a new book, some rules are triggered. Target users are accordingly selected to promote the new book.

  12. Profile association rule discovery • Evaluation

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