10,234 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
Fuzzy Recommendations in Marketing Campaigns
The population in Sweden is growing rapidly due to immigration. In this
light, the issue of infrastructure upgrades to provide telecommunication
services is of importance. New antennas can be installed at hot spots of user
demand, which will require an investment, and/or the clientele expansion can be
carried out in a planned manner to promote the exploitation of the
infrastructure in the less loaded geographical zones. In this paper, we explore
the second alternative. Informally speaking, the term Infrastructure-Stressing
describes a user who stays in the zones of high demand, which are prone to
produce service failures, if further loaded. We have studied the
Infrastructure-Stressing population in the light of their correlation with
geo-demographic segments. This is motivated by the fact that specific
geo-demographic segments can be targeted via marketing campaigns. Fuzzy logic
is applied to create an interface between big data, numeric methods for
processing big data and a manager.Comment: conferenc
Using K-means Clustering and Similarity Measure to Deal with Missing Rating in Collaborative Filtering Recommendation Systems
The Collaborative Filtering recommendation systems have been developed to address the information overload problem and personalize the content to the users for business and organizations. However, the Collaborative Filtering approach has its limitation of data sparsity and online scalability problems which result in low recommendation quality. In this thesis, a novel Collaborative Filtering approach is introduced using clustering and similarity technologies. The proposed method using K-means clustering to partition the entire dataset reduces the time complexity and improves the online scalability as well as the data density. Moreover, the similarity comparison method predicts and fills up the missing value in sparsity dataset to enhance the data density which boosts the recommendation quality. This thesis uses MovieLens dataset to investigate the proposed method, which yields amazing experimental outcome on a large sparsity data set that has a higher quality with lower time complexity than the traditional Collaborative Filtering approaches
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