11,455 research outputs found
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Discovery Is Never By Chance: Designing for (Un)Serendipity
Serendipity has a long tradition in the history of science as having played a key role in many significant discoveries. Computer scientists, valuing the role of serendipity in discovery, have attempted to design systems that encourage serendipity. However, that research has focused primarily on only one aspect of serendipity: that of chance encounters. In reality, for serendipity to be valuable chance encounters must be synthesized into insight. In this paper we show, through a formal consideration of serendipity and analysis of how various systems have seized on attributes of interpreting serendipity, that there is a richer space for design to support serendipitous creativity, innovation and discovery than has been tapped to date. We discuss how ideas might be encoded to be shared or discovered by ‘association-hunting’ agents. We propose considering not only the inventor’s role in perceiving serendipity, but also how that inventor’s perception may be enhanced to increase the opportunity for serendipity. We explore the role of environment and how we can better enable serendipitous discoveries to find a home more readily and immediately
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
TransNFCM: Translation-Based Neural Fashion Compatibility Modeling
Identifying mix-and-match relationships between fashion items is an urgent
task in a fashion e-commerce recommender system. It will significantly enhance
user experience and satisfaction. However, due to the challenges of inferring
the rich yet complicated set of compatibility patterns in a large e-commerce
corpus of fashion items, this task is still underexplored. Inspired by the
recent advances in multi-relational knowledge representation learning and deep
neural networks, this paper proposes a novel Translation-based Neural Fashion
Compatibility Modeling (TransNFCM) framework, which jointly optimizes fashion
item embeddings and category-specific complementary relations in a unified
space via an end-to-end learning manner. TransNFCM places items in a unified
embedding space where a category-specific relation (category-comp-category) is
modeled as a vector translation operating on the embeddings of compatible items
from the corresponding categories. By this way, we not only capture the
specific notion of compatibility conditioned on a specific pair of
complementary categories, but also preserve the global notion of compatibility.
We also design a deep fashion item encoder which exploits the complementary
characteristic of visual and textual features to represent the fashion
products. To the best of our knowledge, this is the first work that uses
category-specific complementary relations to model the category-aware
compatibility between items in a translation-based embedding space. Extensive
experiments demonstrate the effectiveness of TransNFCM over the
state-of-the-arts on two real-world datasets.Comment: Accepted in AAAI 2019 conferenc
A semantic enhanced hybrid recommendation approach: A case study of e-Government tourism service recommendation system
© 2015 Elsevier B.V.All rights reserved. Recommender systems are effectively used as a personalized information filtering technology to automatically predict and identify a set of interesting items on behalf of users according to their personal needs and preferences. Collaborative Filtering (CF) approach is commonly used in the context of recommender systems; however, obtaining better prediction accuracy and overcoming the main limitations of the standard CF recommendation algorithms, such as sparsity and cold-start item problems, remain a significant challenge. Recent developments in personalization and recommendation techniques support the use of semantic enhanced hybrid recommender systems, which incorporate ontology-based semantic similarity measure with other recommendation approaches to improve the quality of recommendations. Consequently, this paper presents the effectiveness of utilizing semantic knowledge of items to enhance the recommendation quality. It proposes a new Inferential Ontology-based Semantic Similarity (IOBSS) measure to evaluate semantic similarity between items in a specific domain of interest by taking into account their explicit hierarchical relationships, shared attributes and implicit relationships. The paper further proposes a hybrid semantic enhanced recommendation approach by combining the new IOBSS measure and the standard item-based CF approach. A set of experiments with promising results validates the effectiveness of the proposed hybrid approach, using a case study of the Australian e-Government tourism services
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