Capturing the Moment: Lightweight Similarity Computations

Similarity computations are crucial in various web activities like advertisements, search or trust-distrust predictions. These similarities often vary with time as product perception and popularity constantly change with users' evolving inclination. The huge volume of user-generated data typically results in heavyweight computations for even a single similarity update. We present I-SIM, a novel similarity metric that enables lightweight similarity computations in an incremental and temporal manner. Incrementality enables updates with low latency whereas temporality captures users' evolving inclination. The main idea behind I-SIM is to disintegrate the similarity metric into mutually independent time-aware factors which can be updated incrementally. We illustrate the efficacy of I-SIM through a novel recommender (SWIFT) as well as through a trust-distrust predictor in Online Social Networks (I-TRUST). We experimentally show that I-SIM enables fast and accurate predictions in an energy-efficient manner.

Publié dans:
2017 IEEE 33rd International Conference on Data Engineering (ICDE), 747-758
Présenté à:
2017 IEEE 33rd International Conference on Data Engineering (ICDE), San Diego, CA, USA, 19-22 April 2017

 Notice créée le 2017-06-28, modifiée le 2019-12-05

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