9,792 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
Explainable Reasoning over Knowledge Graphs for Recommendation
Incorporating knowledge graph into recommender systems has attracted
increasing attention in recent years. By exploring the interlinks within a
knowledge graph, the connectivity between users and items can be discovered as
paths, which provide rich and complementary information to user-item
interactions. Such connectivity not only reveals the semantics of entities and
relations, but also helps to comprehend a user's interest. However, existing
efforts have not fully explored this connectivity to infer user preferences,
especially in terms of modeling the sequential dependencies within and holistic
semantics of a path. In this paper, we contribute a new model named
Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for
recommendation. KPRN can generate path representations by composing the
semantics of both entities and relations. By leveraging the sequential
dependencies within a path, we allow effective reasoning on paths to infer the
underlying rationale of a user-item interaction. Furthermore, we design a new
weighted pooling operation to discriminate the strengths of different paths in
connecting a user with an item, endowing our model with a certain level of
explainability. We conduct extensive experiments on two datasets about movie
and music, demonstrating significant improvements over state-of-the-art
solutions Collaborative Knowledge Base Embedding and Neural Factorization
Machine.Comment: 8 pages, 5 figures, AAAI-201
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender
Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada.
https://recsys.acm.org/recsys18/dlrs
KGAT: Knowledge Graph Attention Network for Recommendation
To provide more accurate, diverse, and explainable recommendation, it is
compulsory to go beyond modeling user-item interactions and take side
information into account. Traditional methods like factorization machine (FM)
cast it as a supervised learning problem, which assumes each interaction as an
independent instance with side information encoded. Due to the overlook of the
relations among instances or items (e.g., the director of a movie is also an
actor of another movie), these methods are insufficient to distill the
collaborative signal from the collective behaviors of users. In this work, we
investigate the utility of knowledge graph (KG), which breaks down the
independent interaction assumption by linking items with their attributes. We
argue that in such a hybrid structure of KG and user-item graph, high-order
relations --- which connect two items with one or multiple linked attributes
--- are an essential factor for successful recommendation. We propose a new
method named Knowledge Graph Attention Network (KGAT) which explicitly models
the high-order connectivities in KG in an end-to-end fashion. It recursively
propagates the embeddings from a node's neighbors (which can be users, items,
or attributes) to refine the node's embedding, and employs an attention
mechanism to discriminate the importance of the neighbors. Our KGAT is
conceptually advantageous to existing KG-based recommendation methods, which
either exploit high-order relations by extracting paths or implicitly modeling
them with regularization. Empirical results on three public benchmarks show
that KGAT significantly outperforms state-of-the-art methods like Neural FM and
RippleNet. Further studies verify the efficacy of embedding propagation for
high-order relation modeling and the interpretability benefits brought by the
attention mechanism.Comment: KDD 2019 research trac
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