3,568 research outputs found
Multi-modal Embedding Fusion-based Recommender
Recommendation systems have lately been popularized globally, with primary
use cases in online interaction systems, with significant focus on e-commerce
platforms. We have developed a machine learning-based recommendation platform,
which can be easily applied to almost any items and/or actions domain. Contrary
to existing recommendation systems, our platform supports multiple types of
interaction data with multiple modalities of metadata natively. This is
achieved through multi-modal fusion of various data representations. We
deployed the platform into multiple e-commerce stores of different kinds, e.g.
food and beverages, shoes, fashion items, telecom operators. Here, we present
our system, its flexibility and performance. We also show benchmark results on
open datasets, that significantly outperform state-of-the-art prior work.Comment: 7 pages, 8 figure
Recommended from our members
Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
Recruitment Market Trend Analysis with Sequential Latent Variable Models
Recruitment market analysis provides valuable understanding of
industry-specific economic growth and plays an important role for both
employers and job seekers. With the rapid development of online recruitment
services, massive recruitment data have been accumulated and enable a new
paradigm for recruitment market analysis. However, traditional methods for
recruitment market analysis largely rely on the knowledge of domain experts and
classic statistical models, which are usually too general to model large-scale
dynamic recruitment data, and have difficulties to capture the fine-grained
market trends. To this end, in this paper, we propose a new research paradigm
for recruitment market analysis by leveraging unsupervised learning techniques
for automatically discovering recruitment market trends based on large-scale
recruitment data. Specifically, we develop a novel sequential latent variable
model, named MTLVM, which is designed for capturing the sequential dependencies
of corporate recruitment states and is able to automatically learn the latent
recruitment topics within a Bayesian generative framework. In particular, to
capture the variability of recruitment topics over time, we design hierarchical
dirichlet processes for MTLVM. These processes allow to dynamically generate
the evolving recruitment topics. Finally, we implement a prototype system to
empirically evaluate our approach based on real-world recruitment data in
China. Indeed, by visualizing the results from MTLVM, we can successfully
reveal many interesting findings, such as the popularity of LBS related jobs
reached the peak in the 2nd half of 2014, and decreased in 2015.Comment: 11 pages, 30 figure, SIGKDD 201
- …