1,910 research outputs found
Measuring the Eccentricity of Items
The long-tail phenomenon tells us that there are many items in the tail.
However, not all tail items are the same. Each item acquires different kinds of
users. Some items are loved by the general public, while some items are
consumed by eccentric fans. In this paper, we propose a novel metric, item
eccentricity, to incorporate this difference between consumers of the items.
Eccentric items are defined as items that are consumed by eccentric users. We
used this metric to analyze two real-world datasets of music and movies and
observed the characteristics of items in terms of eccentricity. The results
showed that our defined eccentricity of an item does not change much over time,
and classified eccentric and noneccentric items present significantly distinct
characteristics. The proposed metric effectively separates the eccentric and
noneccentric items mixed in the tail, which could not be done with the previous
measures, which only consider the popularity of items.Comment: Accepted at IEEE International Conference on Systems, Man, and
Cybernetics (SMC) 201
LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates
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
Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
The rapid proliferation of new users and items on the social web has
aggravated the gray-sheep user/long-tail item challenge in recommender systems.
Historically, cross-domain co-clustering methods have successfully leveraged
shared users and items across dense and sparse domains to improve inference
quality. However, they rely on shared rating data and cannot scale to multiple
sparse target domains (i.e., the one-to-many transfer setting). This, combined
with the increasing adoption of neural recommender architectures, motivates us
to develop scalable neural layer-transfer approaches for cross-domain learning.
Our key intuition is to guide neural collaborative filtering with
domain-invariant components shared across the dense and sparse domains,
improving the user and item representations learned in the sparse domains. We
leverage contextual invariances across domains to develop these shared modules,
and demonstrate that with user-item interaction context, we can learn-to-learn
informative representation spaces even with sparse interaction data. We show
the effectiveness and scalability of our approach on two public datasets and a
massive transaction dataset from Visa, a global payments technology company
(19% Item Recall, 3x faster vs. training separate models for each domain). Our
approach is applicable to both implicit and explicit feedback settings.Comment: SIGIR 202
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