2,841 research outputs found
Learning over Knowledge-Base Embeddings for Recommendation
State-of-the-art recommendation algorithms -- especially the collaborative
filtering (CF) based approaches with shallow or deep models -- usually work
with various unstructured information sources for recommendation, such as
textual reviews, visual images, and various implicit or explicit feedbacks.
Though structured knowledge bases were considered in content-based approaches,
they have been largely neglected recently due to the availability of vast
amount of data, and the learning power of many complex models.
However, structured knowledge bases exhibit unique advantages in personalized
recommendation systems. When the explicit knowledge about users and items is
considered for recommendation, the system could provide highly customized
recommendations based on users' historical behaviors. A great challenge for
using knowledge bases for recommendation is how to integrated large-scale
structured and unstructured data, while taking advantage of collaborative
filtering for highly accurate performance. Recent achievements on knowledge
base embedding sheds light on this problem, which makes it possible to learn
user and item representations while preserving the structure of their
relationship with external knowledge. In this work, we propose to reason over
knowledge base embeddings for personalized recommendation. Specifically, we
propose a knowledge base representation learning approach to embed
heterogeneous entities for recommendation. Experimental results on real-world
dataset verified the superior performance of our approach compared with
state-of-the-art baselines
Social Collaborative Retrieval
Socially-based recommendation systems have recently attracted significant
interest, and a number of studies have shown that social information can
dramatically improve a system's predictions of user interests. Meanwhile, there
are now many potential applications that involve aspects of both recommendation
and information retrieval, and the task of collaborative retrieval---a
combination of these two traditional problems---has recently been introduced.
Successful collaborative retrieval requires overcoming severe data sparsity,
making additional sources of information, such as social graphs, particularly
valuable. In this paper we propose a new model for collaborative retrieval, and
show that our algorithm outperforms current state-of-the-art approaches by
incorporating information from social networks. We also provide empirical
analyses of the ways in which cultural interests propagate along a social graph
using a real-world music dataset.Comment: 10 page
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
Deriving item features relevance from collaborative domain knowledge
An Item based recommender system works by computing a similarity between
items, which can exploit past user interactions (collaborative filtering) or
item features (content based filtering). Collaborative algorithms have been
proven to achieve better recommendation quality then content based algorithms
in a variety of scenarios, being more effective in modeling user behaviour.
However, they can not be applied when items have no interactions at all, i.e.
cold start items. Content based algorithms, which are applicable to cold start
items, often require a lot of feature engineering in order to generate useful
recommendations. This issue is specifically relevant as the content descriptors
become large and heterogeneous. The focus of this paper is on how to use a
collaborative models domain-specific knowledge to build a wrapper feature
weighting method which embeds collaborative knowledge in a content based
algorithm. We present a comparative study for different state of the art
algorithms and present a more general model. This machine learning approach to
feature weighting shows promising results and high flexibility
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