NDLI: Gradient boosting factorization machines

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Gradient boosting factorization machines

Content Provider	ACM Digital Library
Author	King, Irwin Xia, Fen Zhang, Tong Lyu, Michael R. Cheng, Chen
Abstract	Recommendation techniques have been well developed in the past decades. Most of them build models only based on user item rating matrix. However, in real world, there is plenty of auxiliary information available in recommendation systems. We can utilize these information as additional features to improve recommendation performance. We refer to recommendation with auxiliary information as context-aware recommendation. Context-aware Factorization Machines (FM) is one of the most successful context-aware recommendation models. FM models pairwise interactions between all features, in such way, a certain feature latent vector is shared to compute the factorized parameters it involved. In practice, there are tens of context features and not all the pairwise feature interactions are useful. Thus, one important challenge for context-aware recommendation is how to effectively select "good" interaction features. In this paper, we focus on solving this problem and propose a greedy interaction feature selection algorithm based on gradient boosting. Then we propose a novel Gradient Boosting Factorization Machine (GBFM) model to incorporate feature selection algorithm with Factorization Machines into a unified framework. The experimental results on both synthetic and real datasets demonstrate the efficiency and effectiveness of our algorithm compared to other state-of-the-art methods.
Starting Page	265
Ending Page	272
Page Count	8
File Format	PDF MP4
ISBN	9781450326681
DOI	10.1145/2645710.2645730
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-10-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Collaborative filtering Factorization machines Recommender systems Gradient boosting
Content Type	Audio Text
Resource Type	Article

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