5,139 research outputs found
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Toward a Robust Diversity-Based Model to Detect Changes of Context
Being able to automatically and quickly understand the user context during a
session is a main issue for recommender systems. As a first step toward
achieving that goal, we propose a model that observes in real time the
diversity brought by each item relatively to a short sequence of consultations,
corresponding to the recent user history. Our model has a complexity in
constant time, and is generic since it can apply to any type of items within an
online service (e.g. profiles, products, music tracks) and any application
domain (e-commerce, social network, music streaming), as long as we have
partial item descriptions. The observation of the diversity level over time
allows us to detect implicit changes. In the long term, we plan to characterize
the context, i.e. to find common features among a contiguous sub-sequence of
items between two changes of context determined by our model. This will allow
us to make context-aware and privacy-preserving recommendations, to explain
them to users. As this is an ongoing research, the first step consists here in
studying the robustness of our model while detecting changes of context. In
order to do so, we use a music corpus of 100 users and more than 210,000
consultations (number of songs played in the global history). We validate the
relevancy of our detections by finding connections between changes of context
and events, such as ends of session. Of course, these events are a subset of
the possible changes of context, since there might be several contexts within a
session. We altered the quality of our corpus in several manners, so as to test
the performances of our model when confronted with sparsity and different types
of items. The results show that our model is robust and constitutes a promising
approach.Comment: 27th IEEE International Conference on Tools with Artificial
Intelligence (ICTAI 2015), Nov 2015, Vietri sul Mare, Ital
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