9 research outputs found
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
ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance
Most of ranking models are trained only with displayed items (most are hot
items), but they are utilized to retrieve items in the entire space which
consists of both displayed and non-displayed items (most are long-tail items).
Due to the sample selection bias, the long-tail items lack sufficient records
to learn good feature representations, i.e. data sparsity and cold start
problems. The resultant distribution discrepancy between displayed and
non-displayed items would cause poor long-tail performance. To this end, we
propose an entire space adaptation model (ESAM) to address this problem from
the perspective of domain adaptation (DA). ESAM regards displayed and
non-displayed items as source and target domains respectively. Specifically, we
design the attribute correlation alignment that considers the correlation
between high-level attributes of the item to achieve distribution alignment.
Furthermore, we introduce two effective regularization strategies, i.e.
\textit{center-wise clustering} and \textit{self-training} to improve DA
process. Without requiring any auxiliary information and auxiliary domains,
ESAM transfers the knowledge from displayed items to non-displayed items for
alleviating the distribution inconsistency. Experiments on two public datasets
and a large-scale industrial dataset collected from Taobao demonstrate that
ESAM achieves state-of-the-art performance, especially in the long-tail space.
Besides, we deploy ESAM to the Taobao search engine, leading to significant
improvement on online performance. The code is available at
\url{https://github.com/A-bone1/ESAM.git}Comment: Accept by SIGIR-202
Biases in scholarly recommender systems: impact, prevalence, and mitigation
We create a simulated financial market and examine the effect of different levels of active and passive investment on fundamental market efficiency. In our simulated market, active, passive, and random investors interact with each other through issuing orders. Active and passive investors select their portfolio weights by optimizing Markowitz-based utility functions. We find that higher fractions of active investment within a market lead to an increased fundamental market efficiency. The marginal increase in fundamental market efficiency per additional active investor is lower in markets with higher levels of active investment. Furthermore, we find that a large fraction of passive investors within a market may facilitate technical price bubbles, resulting in market failure. By examining the effect of specific parameters on market outcomes, we find that that lower transaction costs, lower individual forecasting errors of active investors, and less restrictive portfolio constraints tend to increase fundamental market efficiency in the market