4,692 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
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
Weighted Random Walk Sampling for Multi-Relational Recommendation
In the information overloaded web, personalized recommender systems are
essential tools to help users find most relevant information. The most
heavily-used recommendation frameworks assume user interactions that are
characterized by a single relation. However, for many tasks, such as
recommendation in social networks, user-item interactions must be modeled as a
complex network of multiple relations, not only a single relation. Recently
research on multi-relational factorization and hybrid recommender models has
shown that using extended meta-paths to capture additional information about
both users and items in the network can enhance the accuracy of recommendations
in such networks. Most of this work is focused on unweighted heterogeneous
networks, and to apply these techniques, weighted relations must be simplified
into binary ones. However, information associated with weighted edges, such as
user ratings, which may be crucial for recommendation, are lost in such
binarization. In this paper, we explore a random walk sampling method in which
the frequency of edge sampling is a function of edge weight, and apply this
generate extended meta-paths in weighted heterogeneous networks. With this
sampling technique, we demonstrate improved performance on multiple data sets
both in terms of recommendation accuracy and model generation efficiency
Personalized Item Ranking from Implicit User Feedback: A Heterogeneous Information Network Approach
In today’s era of the digital world with information overload, generating personalized recommendations for the e-commerce users is a challenging and interesting problem. Recommendation of top-N items of interest to a user of e-commerce is highly challenging using binary implicit feedback. The training data is usually very sparse and have binary values capturing a user’s action or inaction. Due to the sparseness of data and lack of explicit user preferences, the recommendations generated by model-based and neighborhood-based approaches are not effective. Of late, network-based item recommendation methods, which utilize item related meta-information, are beginning to attract increasing attention for binary implicit feedback data. In this work, we propose a heterogeneous information network based recommendation model for personalized top-N recommendations using binary implicit feedback data. To utilize the potential of meta-information related to items, we utilize the concept of meta-path. To improve the effectiveness of the recommendations, the popularity of items and interest of users are leveraged simultaneously. Personalized weight learning of various meta-paths in the network is performed to determine the intrinsic interests of users from the binary implicit feedback data. To show the effectiveness, the proposed model is experimentally evaluated using the real-world dataset.
Available at: https://aisel.aisnet.org/pajais/vol9/iss2/3
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