1,756 research outputs found
Personalized Video Recommendation Using Rich Contents from Videos
Video recommendation has become an essential way of helping people explore
the massive videos and discover the ones that may be of interest to them. In
the existing video recommender systems, the models make the recommendations
based on the user-video interactions and single specific content features. When
the specific content features are unavailable, the performance of the existing
models will seriously deteriorate. Inspired by the fact that rich contents
(e.g., text, audio, motion, and so on) exist in videos, in this paper, we
explore how to use these rich contents to overcome the limitations caused by
the unavailability of the specific ones. Specifically, we propose a novel
general framework that incorporates arbitrary single content feature with
user-video interactions, named as collaborative embedding regression (CER)
model, to make effective video recommendation in both in-matrix and
out-of-matrix scenarios. Our extensive experiments on two real-world
large-scale datasets show that CER beats the existing recommender models with
any single content feature and is more time efficient. In addition, we propose
a priority-based late fusion (PRI) method to gain the benefit brought by the
integrating the multiple content features. The corresponding experiment shows
that PRI brings real performance improvement to the baseline and outperforms
the existing fusion methods
iTrace: An Implicit Trust Inference Method for Trust-aware Collaborative Filtering
The growth of Internet commerce has stimulated the use of collaborative
filtering (CF) algorithms as recommender systems. A collaborative filtering
(CF) algorithm recommends items of interest to the target user by leveraging
the votes given by other similar users. In a standard CF framework, it is
assumed that the credibility of every voting user is exactly the same with
respect to the target user. This assumption is not satisfied and thus may lead
to misleading recommendations in many practical applications. A natural
countermeasure is to design a trust-aware CF (TaCF) algorithm, which can take
account of the difference in the credibilities of the voting users when
performing CF. To this end, this paper presents a trust inference approach,
which can predict the implicit trust of the target user on every voting user
from a sparse explicit trust matrix. Then an improved CF algorithm termed
iTrace is proposed, which takes advantage of both the explicit and the
predicted implicit trust to provide recommendations with the CF framework. An
empirical evaluation on a public dataset demonstrates that the proposed
algorithm provides a significant improvement in recommendation quality in terms
of mean absolute error (MAE).Comment: 6 pages, 4 figures, 1 tabl
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
Collaborative filtering (CF) is the key technique for recommender systems
(RSs). CF exploits user-item behavior interactions (e.g., clicks) only and
hence suffers from the data sparsity issue. One research thread is to integrate
auxiliary information such as product reviews and news titles, leading to
hybrid filtering methods. Another thread is to transfer knowledge from other
source domains such as improving the movie recommendation with the knowledge
from the book domain, leading to transfer learning methods. In real-world life,
no single service can satisfy a user's all information needs. Thus it motivates
us to exploit both auxiliary and source information for RSs in this paper. We
propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH)
methods for cross-domain recommendation with unstructured text in an end-to-end
manner. TMH attentively extracts useful content from unstructured text via a
memory module and selectively transfers knowledge from a source domain via a
transfer network. On two real-world datasets, TMH shows better performance in
terms of three ranking metrics by comparing with various baselines. We conduct
thorough analyses to understand how the text content and transferred knowledge
help the proposed model.Comment: 11 pages, 7 figures, a full version for the WWW 2019 short pape
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
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