Burke, R. (1999). The Wasabi Personal Shopper: A Case-Based Recommender System. In Proceedings of the 11th National Conference on Innovative Applications of Artificial Intelligence, pp. 844-849. AAAI, 1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....(e.g. Pazzani, Muramatsu, Billsus (1996) news stories (e.g. Lang (1995) TV listings (Smyth and Cotter, 1999) and other information sources. A related line of research and development has led to recommendation systems (e.g. Burke, Hammond, and Young (1996) Resnick and Varian (1997) Burke (1999)) which can be used for any task that requires choice among a large set of predefined items. Society, on the other hand, is getting more complex and diversified. The differences in personal preferences, social and educational backgrounds, and private or professional interests are increasing, and ....

Burke R., `The Wasabi Personal Shopper: A Case-Based Recommender System', in: Proceedings of the 16 th National Conference on Artificial Intelligence AAAI99, American Association for Artificial Intelligence, 1999.

....sites (e.g. Pazzani, Muramatsu, Billsus (1996) news stories (e.g. Lang (1995) TV listing (Smyth and Cotter, 1999) and other information sources. A related line of research and development has led to recommendation systems (e.g. Burke, Hammond, and Young (1996) Resnick and Varian (1997) Burke (1999)) which are not limited to filtering information but can be used for any task that requires choice among a large set of predefined items. Society, on the other hand, is getting more complex and diversified. The differences in personal preferences, social and educational backgrounds and private ....

Burke R., `The Wasabi Personal Shopper: A Case-Based Recommender System', in: Proceedings of the 16 th National Conference on Artificial Intelligence AAAI99, American Association for Artificial Intelligence, 1999.

....and private persons who get fast access to the World Wide Web (WWW) Electronic Commerce is playing more and more a central role in economy. Case Based Reasoning (CBR) has become a very important technique for realizing intelligent product recommendation agents for such E Commerce applications [1, 10, 4]. While for fixed, unchangeable products a pure similarity based retrieval approach is sufficient, product customization is important when complex products with a large number of possible variants must be supported during sales [8] Examples of such products are technical products (e.g. personal ....

R. Burke. The wasabi personal shopper: A case-based recommender system. In 11th International Conference on Innovative Applications of Artificial Intelligence, 1999.

....estimation to determine the region s desired size. This work concentrates only on retrieval, however and the retrieved results must still be ranked. Additionally, the work assumes a flat attribute value representation. Entree is a case based restaurant recommendation system available on line 1 [2,3,4]. In this and related systems, we have found that metric application itself can be chief source of inefficiency in retrieval. It is therefore important to create retrieval algorithms that minimize the number of applications of metrics. This paper introduces the problem of metric application that ....

....index technique was used, in which the system retrieved any case that has any possible overlap with the query case. While this technique has the advantage of not ruling out any similar cases, it was found to be excessively liberal, retrieving an average of 87 of the case base. In our later work [3], we implemented a database technique similar to Schumacher and Bergmann s [7] for retrieving this initial set. 3.1 Metric Application On Demand Since metric computations are to be minimized, one of the major concerns regarding the Sort algorithm is that many of the metric computations are ....

Burke, R.: The Wasabi Personal Shopper: A Case-Based Recommender System. In Proceedings of the 11th National Conference on Innovative Applications of Artificial Intelligence, 844-849. AAAI, 1999.

....what features of products matter. It must have access to a product database in which those features are readily discernable or at least inferable. In FindMe systems, we have found this necessity substantial but not onerous. For example, the VintageExchange FindMe recommender system for wines (Burke, 1999) required approximately one person month of knowledge engineering effort. Both Entree and VintageExchange also required significant data cleaning and natural language processing to render database entries useful. In Entree, cross reference data (such lists of all restaurants offering Sunday ....

Burke, R, 1999. The Wasabi Personal Shopper: A CaseBased Recommender System. Submitted to the 11 th Annual Conference on Innovative Applications of Artificial Intelligence.

....items the user will like. 5,6] Knowledge based recommendation employs domain knowledge to infer similarities between items, making this class of recommender systems essentially an application of CBR case retrieval. My research has investigated the creation of knowledge based recommender systems [7,8] that afford critique based navigation through product databases. For example, the restaurant guide Entree 1 [9] allows users to navigate through a database by critiquing restaurants as too expensive, or too traditional, among other dimensions. Each critique invokes a new retrieval redirecting ....

Burke, R. 1999 The Wasabi Personal Shopper: A Case-Based Recommender System. In Proceedings of the 11th National Conference on Innovative Applications of Artificial Intelligence, pp. 844-849, AAAI.

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Burke, R. 1999b. The Wasabi Personal Shopper: A Case-Based Recommender System. In Proceedings of the 11th National Conference on Innovative Applications of Artificial Intelligence, pages 844-849, AAAI.

....or uniform concept. In part, what counts as similar depends on what one s goals are: a shoe is similar to a hammer if one is looking around for something to bang with, but not if one wants to 4 An adapted version of Kenwood was part of the web presence for Kenwood, USA in 19971998. 5 See also (Burke, 1999). 12 extract nails. FindMe similarity measures therefore have to be goal based, and consider multiple goals and their tradeoffs. Typically, there are only a handful of standard goals in any given product domain. For each goal, we define a similarity metric, which measures how closely two ....

Burke, R, 1999. The Wasabi Personal Shopper: A Case-Based Recommender System. In Proceedings the 11th Annual Conference on Innovative Applications of Artificial Intelligence. pp. 844-849. Menlo Park, CA: AAAI Press.

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Burke, R. (1999). The Wasabi Personal Shopper: A Case-Based Recommender System. In Proceedings of the 11th National Conference on Innovative Applications of Artificial Intelligence, pp. 844-849. AAAI, 1999.

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Burke, R. (1999). The Wasabi Personal Shopper: A Case-Based Recommender System. In Proceedings of the 11th National Conference on Innovative Applications of Artificial Intelligence, pp. 844-849. AAAI, 1999.

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