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Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

AAAI 2020 · Architectures and efficiency › RNNs and normalization · approach: Efficient recurrent architectures · 406 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
LSTPM (nonlocal long-term + geo-dilated LSTM short-term) vs best baselinenext-POI recommender · FoursquareRecall@5: 0.3110 -> 0.3372Helped
LSTPM vs best baseline DRCFnext-POI recommender · GowallaRecall@5: 0.1709 -> 0.2021Helped
add short-term module to long-term-only model (ablation)LSTPM · FoursquareRecall@5: 0.3121 -> 0.3372Helped
add long-term nonlocal module to short-term-only model (ablation)LSTPM · FoursquareRecall@5: 0.2187 -> 0.3372Helped
add long-term nonlocal module to short-term-only model (ablation)LSTPM · GowallaRecall@5: 0.1539 -> 0.2021Helped
add geo-dilated LSTM to standard LSTM in short-term module (ablation)LSTPM-S · Foursquare and GowallaRecall/NDCGHelped
longer user historyLSTPM · Foursquare and GowallaRecall/NDCGHelped

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