64 research outputs found
Temporal Learning and Sequence Modeling for a Job Recommender System
We present our solution to the job recommendation task for RecSys Challenge
2016. The main contribution of our work is to combine temporal learning with
sequence modeling to capture complex user-item activity patterns to improve job
recommendations. First, we propose a time-based ranking model applied to
historical observations and a hybrid matrix factorization over time re-weighted
interactions. Second, we exploit sequence properties in user-items activities
and develop a RNN-based recommendation model. Our solution achieved 5
place in the challenge among more than 100 participants. Notably, the strong
performance of our RNN approach shows a promising new direction in employing
sequence modeling for recommendation systems.Comment: a shorter version in proceedings of RecSys Challenge 201
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
Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks
Recommendations can greatly benefit from good representations of the user
state at recommendation time. Recent approaches that leverage Recurrent Neural
Networks (RNNs) for session-based recommendations have shown that Deep Learning
models can provide useful user representations for recommendation. However,
current RNN modeling approaches summarize the user state by only taking into
account the sequence of items that the user has interacted with in the past,
without taking into account other essential types of context information such
as the associated types of user-item interactions, the time gaps between events
and the time of day for each interaction. To address this, we propose a new
class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that
can take into account the contextual information both in the input and output
layers and modifying the behavior of the RNN by combining the context embedding
with the item embedding and more explicitly, in the model dynamics, by
parametrizing the hidden unit transitions as a function of context information.
We compare our CRNNs approach with RNNs and non-sequential baselines and show
good improvements on the next event prediction task
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