5,711 research outputs found
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
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
Signed Distance-based Deep Memory Recommender
Personalized recommendation algorithms learn a user's preference for an item
by measuring a distance/similarity between them. However, some of the existing
recommendation models (e.g., matrix factorization) assume a linear relationship
between the user and item. This approach limits the capacity of recommender
systems, since the interactions between users and items in real-world
applications are much more complex than the linear relationship. To overcome
this limitation, in this paper, we design and propose a deep learning framework
called Signed Distance-based Deep Memory Recommender, which captures non-linear
relationships between users and items explicitly and implicitly, and work well
in both general recommendation task and shopping basket-based recommendation
task. Through an extensive empirical study on six real-world datasets in the
two recommendation tasks, our proposed approach achieved significant
improvement over ten state-of-the-art recommendation models
Off-line vs. On-line Evaluation of Recommender Systems in Small E-commerce
In this paper, we present our work towards comparing on-line and off-line
evaluation metrics in the context of small e-commerce recommender systems.
Recommending on small e-commerce enterprises is rather challenging due to the
lower volume of interactions and low user loyalty, rarely extending beyond a
single session. On the other hand, we usually have to deal with lower volumes
of objects, which are easier to discover by users through various
browsing/searching GUIs.
The main goal of this paper is to determine applicability of off-line
evaluation metrics in learning true usability of recommender systems (evaluated
on-line in A/B testing). In total 800 variants of recommending algorithms were
evaluated off-line w.r.t. 18 metrics covering rating-based, ranking-based,
novelty and diversity evaluation. The off-line results were afterwards compared
with on-line evaluation of 12 selected recommender variants and based on the
results, we tried to learn and utilize an off-line to on-line results
prediction model.
Off-line results shown a great variance in performance w.r.t. different
metrics with the Pareto front covering 68\% of the approaches. Furthermore, we
observed that on-line results are considerably affected by the novelty of
users. On-line metrics correlates positively with ranking-based metrics (AUC,
MRR, nDCG) for novice users, while too high values of diversity and novelty had
a negative impact on the on-line results for them. For users with more visited
items, however, the diversity became more important, while ranking-based
metrics relevance gradually decrease.Comment: Submitted to ACM Hypertext 2020 Conferenc
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