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Making Meaningful Restaurant Recommendations At OpenTable

Published: 16 September 2015 Publication History

Abstract

At OpenTable, recommendations play a key role in connecting diners with restaurants. The act of recommending a restaurant to a diner relies heavily on aligning everything we know about the restaurant with everything we can infer about the diner. Our methods go beyond using the diner-restaurant interaction history as the sole input -- we use click and search data, the metadata of restaurants, as well as insights gleaned from reviews, together with any contextual information to make meaningful recommendations. In this talk, I will highlight the main aspects of our recommendation stack built with Scala using Apache Spark.

Cited By

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  • (2019)Adversarial Substructured Representation Learning for Mobile User ProfilingProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330869(130-138)Online publication date: 25-Jul-2019
  • (2019)Multi-criteria Recommendations by Using Criteria Preferences as ContextsTowards Integrated Web, Mobile, and IoT Technology10.1007/978-3-030-28430-5_2(21-35)Online publication date: 10-Aug-2019

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  1. Making Meaningful Restaurant Recommendations At OpenTable

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    cover image ACM Conferences
    RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems
    September 2015
    414 pages
    ISBN:9781450336925
    DOI:10.1145/2792838
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 16 September 2015

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    Author Tags

    1. content analysis
    2. matrix factorization
    3. recommendation

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    • Invited-talk

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    RecSys '15
    Sponsor:
    RecSys '15: Ninth ACM Conference on Recommender Systems
    September 16 - 20, 2015
    Vienna, Austria

    Acceptance Rates

    RecSys '15 Paper Acceptance Rate 28 of 131 submissions, 21%;
    Overall Acceptance Rate 254 of 1,295 submissions, 20%

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    Cited By

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    • (2019)Adversarial Substructured Representation Learning for Mobile User ProfilingProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330869(130-138)Online publication date: 25-Jul-2019
    • (2019)Multi-criteria Recommendations by Using Criteria Preferences as ContextsTowards Integrated Web, Mobile, and IoT Technology10.1007/978-3-030-28430-5_2(21-35)Online publication date: 10-Aug-2019

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