会議発表論文

会議発表論文

text

RecSys 2021 - 15th ACM Conference on Recommender Systems. 2021, p. 273-281

Reverse maximum inner product search: How to efficiently find users Who would like to buy my item?

Association for Computing Machinery, Inc

英語

text

2021-09-13

https://doi.org/10.1145/3460231.3474229

The MIPS (maximum inner product search), which finds the item with the highest inner product with a given query user, is an essential problem in the recommendation field. It is usual that e-commerce companies face situations where they want to promote and sell new or discounted items. In these situations, we have to consider a question: who are interested in the items and how to find them? This paper answers this question by addressing a new problem called reverse maximum inner product search (reverse MIPS). Given a query vector and two sets of vectors (user vectors and item vectors), the problem of reverse MIPS finds a set of user vectors whose inner product with the query vector is the maximum among the query and item vectors. Although the importance of this problem is clear, its straightforward implementation incurs a computationally expensive cost. We therefore propose Simpfer, a simple, fast, and exact algorithm for reverse MIPS. In an offline phase, Simpfer builds a simple index that maintains a lower-bound of the maximum inner product. By exploiting this index, Simpfer judges whether the query vector can have the maximum inner product or not, for a given user vector, in a constant time. Besides, our index enables filtering user vectors, which cannot have the maximum inner product with the query vector, in a batch. We theoretically demonstrate that Simpfer outperforms baselines employing state-of-the-art MIPS techniques. Furthermore, our extensive experiments on real datasets show that Simpfer is about 500-8000 times faster than the baselines.

© 2021 ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Amagata D., Hara T.. Reverse maximum inner product search: How to efficiently find users Who would like to buy my item?. RecSys 2021 - 15th ACM Conference on Recommender Systems , 273 (2021); https://doi.org/10.1145/3460231.3474229.

92847

Reverse maximum inner product search: How to efficiently find users Who would like to buy my item?

Association for Computing Machinery, Inc

英語

2021-09-13

RecSys 2021 - 15th ACM Conference on Recommender Systems

273

281

https://doi.org/10.1145/3460231.3474229

https://hdl.handle.net/11094/92847