LambdaFM

Abstract

State-of-the-art item recommendation algorithms, which apply Factorization Machines (FM) as a scoring function and pairwise ranking loss as a trainer (PRFM for short), have been recently investigated for the implicit feedback based context-aware recommendation problem (IFCAR). However, good recommenders particularly emphasize on the accuracy near the top of the ranked list, and typical pairwise loss functions might not match well with such a requirement. In this paper, we demonstrate, both theoretically and empirically, PRFM models usually lead to non-optimal item recommendation results due to such a mismatch. Inspired by the success of LambdaRank, we introduce Lambda Factorization Machines (LambdaFM), which is particularly intended for optimizing ranking performance for IFCAR. We also point out that the original lambda function suffers from the issue of expensive computational complexity in such settings due to a large amount of unobserved feedback. Hence, instead of directly adopting the original lambda strategy, we create three effective lambda surrogates by conducting a theoretical analysis for lambda from the top-N optimization perspective. Further, we prove that the proposed lambda surrogates are generic and applicable to a large set of pairwise ranking loss functions. Experimental results demonstrate LambdaFM significantly outperforms state-of-the-art algorithms on three real-world datasets in terms of four standard ranking measures.

An efficient recommender system based on improved KGPolicy module

“…Later on, adaptive samplers, such as DNS [6] and LamdaFM [15], select hard negatives to train recommendation model and bring some changes to current model parameters. Some studies [2], [7] apply generative adversarial networks (GANs) to yield adversarial negative items.…”

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

“…[LambdaGAN] In [128], the authors propose LambdaGAN -a GAN model with a lambda ranking strategy-that improves the recommendation performance in a pairwise ranking setting by proposing lambda rank [140] function into the adversarial learning of the proposed GAN-based CF framework.…”