17,889 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
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
Survey on Service Based Ratings of Users by Exploring Geographical Location
Recommendation systems help online users with advantageous access to the items and services they may be intrested on this present reality. Because of the requirements of compelling forecast and productive recommendation, it is advantageous for the location-based services (LBS), to discover the user's next location that the user may visit. So in this paper, diverse kinds of methodologies used to discover, anticipate, and examine location based services are talked about. It is important to convey those expectation and recommendation services for ongoing real time application with direction mapping. While considering location information's, at that point the information measure ended up noticeably colossal and dynamic. Finding ideal answer for anticipate the rating in view of the location and unequivocal conduct is overviewed
NAIS: Neural Attentive Item Similarity Model for Recommendation
Item-to-item collaborative filtering (aka. item-based CF) has been long used
for building recommender systems in industrial settings, owing to its
interpretability and efficiency in real-time personalization. It builds a
user's profile as her historically interacted items, recommending new items
that are similar to the user's profile. As such, the key to an item-based CF
method is in the estimation of item similarities. Early approaches use
statistical measures such as cosine similarity and Pearson coefficient to
estimate item similarities, which are less accurate since they lack tailored
optimization for the recommendation task. In recent years, several works
attempt to learn item similarities from data, by expressing the similarity as
an underlying model and estimating model parameters by optimizing a
recommendation-aware objective function. While extensive efforts have been made
to use shallow linear models for learning item similarities, there has been
relatively less work exploring nonlinear neural network models for item-based
CF.
In this work, we propose a neural network model named Neural Attentive Item
Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an
attention network, which is capable of distinguishing which historical items in
a user profile are more important for a prediction. Compared to the
state-of-the-art item-based CF method Factored Item Similarity Model (FISM),
our NAIS has stronger representation power with only a few additional
parameters brought by the attention network. Extensive experiments on two
public benchmarks demonstrate the effectiveness of NAIS. This work is the first
attempt that designs neural network models for item-based CF, opening up new
research possibilities for future developments of neural recommender systems
Trust beyond reputation: A computational trust model based on stereotypes
Models of computational trust support users in taking decisions. They are
commonly used to guide users' judgements in online auction sites; or to
determine quality of contributions in Web 2.0 sites. However, most existing
systems require historical information about the past behavior of the specific
agent being judged. In contrast, in real life, to anticipate and to predict a
stranger's actions in absence of the knowledge of such behavioral history, we
often use our "instinct"- essentially stereotypes developed from our past
interactions with other "similar" persons. In this paper, we propose
StereoTrust, a computational trust model inspired by stereotypes as used in
real-life. A stereotype contains certain features of agents and an expected
outcome of the transaction. When facing a stranger, an agent derives its trust
by aggregating stereotypes matching the stranger's profile. Since stereotypes
are formed locally, recommendations stem from the trustor's own personal
experiences and perspective. Historical behavioral information, when available,
can be used to refine the analysis. According to our experiments using
Epinions.com dataset, StereoTrust compares favorably with existing trust models
that use different kinds of information and more complete historical
information
์์ ๋คํธ์ํฌ์ ์ด์ปค๋จธ์ค ํ๋ซํผ์์์ ์ ์ฌ ๋คํธ์ํฌ ๋ง์ด๋
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ์ปดํจํฐ๊ณตํ๋ถ, 2023. 2. ๊ถํ๊ฒฝ.์น ๊ธฐ๋ฐ ์๋น์ค์ ํญ๋ฐ์ ์ธ ๋ฐ๋ฌ๋ก ์ฌ์ฉ์๋ค์ ์จ๋ผ์ธ ์์์ ํญ๋๊ฒ ์ฐ๊ฒฐ๋๊ณ ์๋ค. ์จ๋ผ์ธ ํ๋ซํผ ์์์, ์ฌ์ฉ์๋ค์ ์๋ก์๊ฒ ์ํฅ์ ์ฃผ๊ณ ๋ฐ์ผ๋ฉฐ ์์ฌ ๊ฒฐ์ ์ ๊ทธ๋ค์ ๊ฒฝํ๊ณผ ์๊ฒฌ์ ๋ฐ์ํ๋ ๊ฒฝํฅ์ ๋ณด์ธ๋ค. ๋ณธ ํ์ ๋
ผ๋ฌธ์์๋ ๋ํ์ ์ธ ์จ๋ผ์ธ ํ๋ซํผ์ธ ์์
๋คํธ์ํฌ ์๋น์ค์ ์ด์ปค๋จธ์ค ํ๋ซํผ์์์ ์ฌ์ฉ์ ํ๋์ ๋ํด ์ฐ๊ตฌํ์๋ค.
์จ๋ผ์ธ ํ๋ซํผ์์์ ์ฌ์ฉ์ ํ๋์ ์ฌ์ฉ์์ ํ๋ซํผ ๊ตฌ์ฑ ์์ ๊ฐ์ ๊ด๊ณ๋ก ํํํ ์ ์๋ค. ์ฌ์ฉ์์ ๊ตฌ๋งค๋ ์ฌ์ฉ์์ ์ํ ๊ฐ์ ๊ด๊ณ๋ก, ์ฌ์ฉ์์ ์ฒดํฌ์ธ์ ์ฌ์ฉ์์ ์ฅ์ ๊ฐ์ ๊ด๊ณ๋ก ๋ํ๋ด์ง๋ค. ์ฌ๊ธฐ์ ํ๋์ ์๊ฐ๊ณผ ๋ ์ดํ
, ํ๊ทธ ๋ฑ์ ์ ๋ณด๊ฐ ํฌํจ๋ ์ ์๋ค.
๋ณธ ์ฐ๊ตฌ์์๋ ๋ ํ๋ซํผ์์ ์ ์๋ ์ฌ์ฉ์์ ํ๋ ๊ทธ๋ํ์ ์ํฅ์ ๋ฏธ์น๋ ์ ์ฌ ๋คํธ์ํฌ๋ฅผ ํ์
ํ๋ ์ฐ๊ตฌ๋ฅผ ์ ์ํ๋ค. ์์น ๊ธฐ๋ฐ์ ์์
๋คํธ์ํฌ ์๋น์ค์ ๊ฒฝ์ฐ ํน์ ์ฅ์์ ๋ฐฉ๋ฌธํ๋ ์ฒดํฌ์ธ ํ์์ผ๋ก ๋ง์ ํฌ์คํธ๊ฐ ๋ง๋ค์ด์ง๋๋ฐ, ์ฌ์ฉ์์ ์ฅ์ ๋ฐฉ๋ฌธ์ ์ฌ์ฉ์ ๊ฐ์ ์ฌ์ ์ ์กด์ฌํ๋ ์น๊ตฌ ๊ด๊ณ์ ์ํด ์ํฅ์ ํฌ๊ฒ ๋ฐ๋๋ค. ์ฌ์ฉ์ ํ๋ ๋คํธ์ํฌ์ ์ ๋ณ์ ์ ์ฌ๋ ์ฌ์ฉ์ ๊ฐ์ ๊ด๊ณ๋ฅผ ํ์
ํ๋ ๊ฒ์ ํ๋ ์์ธก์ ๋์์ด ๋ ์ ์์ผ๋ฉฐ, ์ด๋ฅผ ์ํด ๋ณธ ๋
ผ๋ฌธ์์๋ ๋น์ง๋ํ์ต ๊ธฐ๋ฐ์ผ๋ก ํ๋ ๋คํธ์ํฌ๋ก๋ถํฐ ์ฌ์ฉ์ ๊ฐ ์ฌํ์ ๊ด๊ณ๋ฅผ ์ถ์ถํ๋ ์ฐ๊ตฌ๋ฅผ ์ ์ํ์๋ค.
๊ธฐ์กด์ ์ฐ๊ตฌ๋์๋ ๋ฐฉ๋ฒ๋ค์ ๋ ์ฌ์ฉ์๊ฐ ๋์์ ๋ฐฉ๋ฌธํ๋ ํ์์ธ co-visitation์ ์ค์ ์ ์ผ๋ก ๊ณ ๋ คํ์ฌ ์ฌ์ฉ์ ๊ฐ์ ๊ด๊ณ๋ฅผ ์์ธกํ๊ฑฐ๋, ๋คํธ์ํฌ ์๋ฒ ๋ฉ ๋๋ ๊ทธ๋ํ ์ ๊ฒฝ๋ง(GNN)์ ์ฌ์ฉํ์ฌ ํํ ํ์ต์ ์ํํ์๋ค. ๊ทธ๋ฌ๋ ์ด๋ฌํ ์ ๊ทผ ๋ฐฉ์์ ์ฃผ๊ธฐ์ ์ธ ๋ฐฉ๋ฌธ์ด๋ ์ฅ๊ฑฐ๋ฆฌ ์ด๋ ๋ฑ์ผ๋ก ๋ํ๋๋ ์ฌ์ฉ์์ ํ๋ ํจํด์ ์ ํฌ์ฐฉํ์ง ๋ชปํ๋ค. ํ๋ ํจํด์ ๋ ์ ํ์ตํ๊ธฐ ์ํด, ANES๋ ์ฌ์ฉ์ ์ปจํ
์คํธ ๋ด์์ ์ฌ์ฉ์์ ๊ด์ฌ ์ง์ (POI) ๊ฐ์ ์ธก๋ฉด(Aspect) ์งํฅ ๊ด๊ณ๋ฅผ ํ์ตํ๋ค. ANES๋ User-POI ์ด๋ถ ๊ทธ๋ํ์ ๊ตฌ์กฐ์์ ์ฌ์ฉ์์ ํ๋์ ์ฌ๋ฌ ๊ฐ์ ์ธก๋ฉด์ผ๋ก ๋๋๊ณ , ๊ฐ๊ฐ์ ๊ด๊ณ๋ฅผ ๊ณ ๋ คํ์ฌ ํ๋ ํจํด์ ์ถ์ถํ๋ ์ต์ด์ ๋น์ง๋ํ์ต ๊ธฐ๋ฐ ์ ๊ทผ ๋ฐฉ์์ด๋ค. ์ค์ LBSN ๋ฐ์ดํฐ์์ ์ํ๋ ๊ด๋ฒ์ํ ์คํ์์, ANES๋ ๊ธฐ์กด์ ์ ์๋์๋ ๊ธฐ๋ฒ๋ค๋ณด๋ค ๋์ ์ฑ๋ฅ์ ๋ณด์ฌ์ค๋ค.
์์น ๊ธฐ๋ฐ ์์
๋คํธ์ํฌ์๋ ๋ค๋ฅด๊ฒ, ์ด์ปค๋จธ์ค์ ๋ฆฌ๋ทฐ ์์คํ
์์๋ ์ฌ์ฉ์๋ค์ด ๋ฅ๋์ ์ธ ํ๋ก์ฐ/ํ๋ก์ ๋ฑ์ ํ์๋ฅผ ์ํํ์ง ์๊ณ ๋ ํ๋ซํผ์ ์ํด ์๋ก์ ์ ๋ณด๋ฅผ ์ฃผ๊ณ ๋ฐ๊ณ ์ํฅ๋ ฅ์ ํ์ฌํ๊ฒ ๋๋ค. ์ด์ ๊ฐ์ ์ฌ์ฉ์๋ค์ ํ๋ ํน์ฑ์ ๋ฆฌ๋ทฐ ์คํธ์ ์ํด ์ฝ๊ฒ ์
์ฉ๋ ์ ์๋ค. ๋ฆฌ๋ทฐ ์คํธ์ ์ค์ ์ฌ์ฉ์์ ์๊ฒฌ์ ์จ๊ธฐ๊ณ ํ์ ์ ์กฐ์ํ์ฌ ์๋ชป๋ ์ ๋ณด๋ฅผ ์ ๋ฌํ๋ ๋ฐฉ์์ผ๋ก ์ด๋ฃจ์ด์ง๋ค. ๋๋ ์ด๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด ์ฌ์ฉ์ ๋ฆฌ๋ทฐ ๋ฐ์ดํฐ์์ ์ฌ์ฉ์ ๊ฐ ์ฌ์ ๊ณต๋ชจ์ฑ(Collusiveness)์ ๊ฐ๋ฅ์ฑ์ ์ฐพ๊ณ , ์ด๋ฅผ ์คํธ ํ์ง์ ํ์ฉํ ๋ฐฉ๋ฒ์ธ SC-Com์ ์ ์ํ๋ค. SC-Com์ ํ๋์ ๊ณต๋ชจ์ฑ์ผ๋ก๋ถํฐ ์ฌ์ฉ์ ๊ฐ ๊ณต๋ชจ ์ ์๋ฅผ ๊ณ์ฐํ๊ณ ํด๋น ์ ์๋ฅผ ๋ฐํ์ผ๋ก ์ ์ฒด ์ฌ์ฉ์๋ฅผ ์ ์ฌํ ์ฌ์ฉ์๋ค์ ์ปค๋ฎค๋ํฐ๋ก ๋ถ๋ฅํ๋ค. ๊ทธ ํ ์คํธ ์ ์ ์ ์ผ๋ฐ ์ ์ ๋ฅผ ๊ตฌ๋ณํ๋ ๋ฐ์ ์ค์ํ ๊ทธ๋ํ ๊ธฐ๋ฐ์ ํน์ง์ ์ถ์ถํ์ฌ ๊ฐ๋
ํ์ต ๊ธฐ๋ฐ์ ๋ถ๋ฅ๊ธฐ์ ์
๋ ฅ ๋ฐ์ดํฐ๋ก ํ์ฉํ๋ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. SC-Com์ ๊ณต๋ชจ์ฑ์ ๊ฐ๋ ์คํธ ์ ์ ์ ์งํฉ์ ํจ๊ณผ์ ์ผ๋ก ํ์งํ๋ค. ์ค์ ๋ฐ์ดํฐ์
์ ์ด์ฉํ ์คํ์์, SC-Com์ ๊ธฐ์กด ๋
ผ๋ฌธ๋ค ๋๋น ์คํธ ํ์ง์ ๋ฐ์ด๋ ์ฑ๋ฅ์ ๋ณด์ฌ์ฃผ์๋ค.
์ ๋
ผ๋ฌธ์์ ๋ค์ํ ๋ฐ์ดํฐ์ ๋ํด ์ฐ๊ตฌ๋ ์์์ ์ฐ๊ฒฐ๋ง ํ์ง ๋ชจ๋ธ์ ๋ ์ด๋ธ์ด ์๋ ๋ฐ์ดํฐ์ ๋ํด์๋ ์ฌ์ ์ ์ฐ๊ฒฐ๋์์ ๊ฐ๋ฅ์ฑ์ด ๋์ ์ฌ์ฉ์๋ค์ ์์ธกํ๋ฏ๋ก, ์ค์๊ฐ ์์น ๋ฐ์ดํฐ๋, ์ฑ ์ฌ์ฉ ๋ฐ์ดํฐ ๋ฑ์ ๋ค์ํ ๋ฐ์ดํฐ์์ ํ์ฉํ ์ ์๋ ์ ์ฉํ ์ ๋ณด๋ฅผ ์ ๊ณตํ์ฌ ๊ด๊ณ ์ถ์ฒ ์์คํ
์ด๋, ์
์ฑ ์ ์ ํ์ง ๋ฑ์ ๋ถ์ผ์์ ๊ธฐ์ฌํ ์ ์์ ๊ฒ์ผ๋ก ๊ธฐ๋ํ๋ค.Following the exploding usage on online services, people are connected with each other more broadly and widely. In online platforms, people influence each other, and have tendency to reflect their opinions in decision-making. Social Network Services (SNSs) and E-commerce are typical example of online platforms.
User behaviors in online platforms can be defined as relation between user and platform components. A user's purchase is a relationship between a user and a product, and a user's check-in is a relationship between a user and a place. Here, information such as action time, rating, tag, etc. may be included. In many studies, platform user behavior is represented in graph form. At this time, the elements constituting the nodes of the graph are composed of objects such as users and products and places within the platform, and the interaction between the platform elements and the user can be expressed as two nodes being connected.
In this study, I present studies to identify potential networks that affect the user's behavior graph defined on the two platforms.
In ANES, I focus on representation learning for social link inference based on user trajectory data. While traditional methods predict relations between users by considering hand-crafted features, recent studies first perform representation learning using network/node embedding or graph neural networks (GNNs) for downstream tasks such as node classification and link prediction. However, those approaches fail to capture behavioral patterns of individuals ingrained in periodical visits or long-distance movements. To better learn behavioral patterns, this paper proposes a novel scheme called ANES (Aspect-oriented Network Embedding for Social link inference). ANES learns aspect-oriented relations between users and Point-of-Interests (POIs) within their contexts. ANES is the first approach that extracts the complex behavioral pattern of users from both trajectory data and the structure of User-POI bipartite graphs. Extensive experiments on several real-world datasets show that ANES outperforms state-of-the-art baselines.
In contrast to active social networks, people are connected to other users regardless of their intentions in some platforms, such as online shopping websites and restaurant review sites. They do not have any information about each other in advance, and they only have a common point which is that they have visited or have planned to visit same place or purchase a product. Interestingly, users have tendency to be influenced by the review data on their purchase intentions.
Unfortunately, this instinct is easily exploited by opinion spammers. In SC-Com, I focus on opinion spam detection in online shopping services. In many cases, my decision-making process is closely related to online reviews. However, there have been threats of opinion spams by hired reviewers increasingly, which aim to mislead potential customers by hiding genuine consumers opinions. Opinion spams should be filed up collectively to falsify true information. Fortunately, I propose the way to spot the possibility to detect them from their collusiveness. In this paper, I propose SC-Com, an optimized collusive community detection framework. It constructs the graph of reviewers from the collusiveness of behavior and divides a graph by communities based on their mutual suspiciousness. After that, I extract community-based and temporal abnormality features which are critical to discriminate spammers from other genuine users. I show that my method detects collusive opinion spam reviewers effectively and precisely from their collective behavioral patterns. In the real-world dataset, my approach showed prominent performance while only considering primary data such as time and ratings.
These implicit network inference models studied on various data in this thesis predicts users who are likely to be pre-connected to unlabeled data, so it is expected to contribute to areas such as advertising recommendation systems and malicious user detection by providing useful information.Chapter 1 Introduction 1
Chapter 2 Social link Inference in Location-based check-in data 5
2.1 Background 5
2.2 Related Work 12
2.3 Location-based Social Network Service Data 15
2.4 Aspect-wise Graph Decomposition 18
2.5 Aspect-wise Graph learning 19
2.6 Inferring Social Relation from User Representation 21
2.7 Performance Analysis 23
2.8 Discussion and Implications 26
2.9 Summary 34
Chapter 3 Detecting collusiveness from reviews in Online platforms and its application 35
3.1 Background 35
3.2 Related Work 39
3.3 Online Review Data 43
3.4 Collusive Graph Projection 44
3.5 Reviewer Community Detection 47
3.6 Review Community feature extraction and spammer detection 51
3.7 Performance Analysis 53
3.8 Discussion and Implications 55
3.9 Summary 62
Chapter 4 Conclusion 63๋ฐ
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
Recommendation systems are ubiquitous and impact many domains; they have the
potential to influence product consumption, individuals' perceptions of the
world, and life-altering decisions. These systems are often evaluated or
trained with data from users already exposed to algorithmic recommendations;
this creates a pernicious feedback loop. Using simulations, we demonstrate how
using data confounded in this way homogenizes user behavior without increasing
utility
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