Probabilistic Bloom Filter for Online Learning in Personalized Recommender Systems

다국어 초록 (Multilingual Abstract)

Bloom filters are used as embeddings in machine learning domain because of its characteristic of remembering items with less storage. As to online learning, a specific kind of bloom filter is needed, which can prevent filters being full as new items appear by decay. We compared bloom filters which can decay in perspective of precision and the degree of preservation of similarities between items using dataset for personalized recommendation. We also suggested a new bloom filter named as 'probabilistic bloom filter', which has two advantages. First, it showed higher precision than other filters at the expense of similarities between items while overall performance was the second to 'stable bloom filter' when it comes to precision-similarity trade-off. Second, even in a long term, it ensures that filters are not full or empty without continuous parameter calibration.

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