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Numerical study of reciprocal recommendation with domain matching

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  • Computational statistics and machine learning
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Abstract

Reciprocal recommendation is the task of finding preferable matches among users in two distinct groups. Popular examples of reciprocal recommendation include online job recruiting and online dating services. In this paper, we propose a new method of reciprocal recommendation that uses a graph embedding technique. In particular, we use cross-domain matching correlation analysis (CDMCA) as a graph embedding method. In CDMCA, feature vectors in different domains are mapped into a common representation space, and reciprocal recommendation is conducted in the common mapped space. Numerical experiments show that the CDMCA with a similarity-based weighting scheme provides a high-quality reciprocal recommendation.

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Acknowledgements

TK was supported by KAKENHI 16K00044, 15H03636, and 15H01678.

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Authors and Affiliations

  1. NS Solutions Corporation, NBF Shinkawa Building 20-15, Shinkawa 2-chome, Chuo-ku, Tokyo, 104-8280, Japan

    Kotaro Sudo

  2. Recruit Technologies Co., Ltd., GranTokyo South Tower, 1-9-2 Marunouchi, Chiyoda-ku, Tokyo, 100-6640, Japan

    Naoya Osugi

  3. Tokyo Institute of Technology/RIKEN AIP, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8552, Japan

    Takafumi Kanamori

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  1. Kotaro Sudo
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  2. Naoya Osugi
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  3. Takafumi Kanamori
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Correspondence to Takafumi Kanamori.

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Sudo, K., Osugi, N. & Kanamori, T. Numerical study of reciprocal recommendation with domain matching. Jpn J Stat Data Sci 2, 221–240 (2019). https://doi.org/10.1007/s42081-019-00033-3

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