Need Love? There's a New Algorithm for That.

Zhao’s crucial innovation is to combine information about both tastes and attractiveness. The algorithm keeps track of both who I am messaging, and who is messaging me. If a male user has similar taste (he is messaging the same women as I am) and attractiveness (he is messaged by the same women as I am) to me, we are scored as being very similar; if we are similar in one trait — if we have similar tastes but attract (or fail to attract) different groups of women, or vice versa — we have a moderate similarity ranking, and if we are different on both measures, we are counted as very dissimilar.

Similarly, when finding women to recommend to me, the algorithm factors in both sides of the messaging coin. Women who had a back-and-forth messaging relationship with men similar to me are ranked very highly, women who had a one-sided messaging relationship with men similar to me are ranked in the middle, and women who have had no contact on either side with similar men are left out.

Zhao and his peers tested their hybrid algorithm, incorporating both taste and attractiveness information, on an unnamed popular dating site, and found that it outperformed a number of other recommender models. The algorithm did a very solid job in recommending potential matches that, if messaged, would message users back.

Now if only Zhao would give up which site he tinkered with and this could be a very happy holiday season.

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