Subjective Similarity: Personalizing Alternative Item Recommendations
Pages 1275 - 1279
Abstract
We present a new algorithm for recommending alternatives to a given item in an e-commerce setting. Our algorithm is an incremental improvement over an earlier system, which recommends similar items by first assigning the input item to clusters and then selecting best quality items within those clusters. The original algorithm does not consider the recent context and our new algorithm improves the earlier system by personalizing the recommendations to user intentions. The system measures user intention using the recent queries, which are used to determine the level of abstraction in similarity and relative importance of similarity dimensions. We show that user engagement increases when recommended item titles share more terms with most recent queries. Moreover, the new algorithm increases query coverage without sacrificing input item similarity and item quality.
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Index Terms
- Subjective Similarity: Personalizing Alternative Item Recommendations
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Published In
May 2015
1602 pages
ISBN:9781450334730
DOI:10.1145/2740908
- General Chairs:
- Aldo Gangemi,
- Stefano Leonardi,
- Alessandro Panconesi
Copyright © 2015 Copyright is held by the International World Wide Web Conference Committee (IW3C2).
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- IW3C2: International World Wide Web Conference Committee
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Association for Computing Machinery
New York, NY, United States
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Published: 18 May 2015
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WWW '15
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- IW3C2
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