365 research outputs found
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions
This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations
Multi-dimensional clustering in user profiling
User profiling has attracted an enormous number of technological methods and
applications. With the increasing amount of products and services, user profiling
has created opportunities to catch the attention of the user as well as achieving
high user satisfaction. To provide the user what she/he wants, when and how,
depends largely on understanding them. The user profile is the representation of
the user and holds the information about the user. These profiles are the
outcome of the user profiling.
Personalization is the adaptation of the services to meet the user’s needs and
expectations. Therefore, the knowledge about the user leads to a personalized
user experience. In user profiling applications the major challenge is to build and
handle user profiles. In the literature there are two main user profiling methods,
collaborative and the content-based. Apart from these traditional profiling
methods, a number of classification and clustering algorithms have been used
to classify user related information to create user profiles. However, the profiling,
achieved through these works, is lacking in terms of accuracy. This is because,
all information within the profile has the same influence during the profiling even
though some are irrelevant user information.
In this thesis, a primary aim is to provide an insight into the concept of user
profiling. For this purpose a comprehensive background study of the literature
was conducted and summarized in this thesis. Furthermore, existing user
profiling methods as well as the classification and clustering algorithms were investigated. Being one of the objectives of this study, the use of these
algorithms for user profiling was examined. A number of classification and
clustering algorithms, such as Bayesian Networks (BN) and Decision Trees
(DTs) have been simulated using user profiles and their classification accuracy
performances were evaluated. Additionally, a novel clustering algorithm for the
user profiling, namely Multi-Dimensional Clustering (MDC), has been proposed.
The MDC is a modified version of the Instance Based Learner (IBL) algorithm.
In IBL every feature has an equal effect on the classification regardless of their
relevance. MDC differs from the IBL by assigning weights to feature values to
distinguish the effect of the features on clustering. Existing feature weighing
methods, for instance Cross Category Feature (CCF), has also been
investigated. In this thesis, three feature value weighting methods have been
proposed for the MDC. These methods are; MDC weight method by Cross
Clustering (MDC-CC), MDC weight method by Balanced Clustering (MDC-BC)
and MDC weight method by changing the Lower-limit to Zero (MDC-LZ). All of
these weighted MDC algorithms have been tested and evaluated. Additional
simulations were carried out with existing weighted and non-weighted IBL
algorithms (i.e. K-Star and Locally Weighted Learning (LWL)) in order to
demonstrate the performance of the proposed methods. Furthermore, a real life scenario is implemented to show how the MDC can be used for the user
profiling to improve personalized service provisioning in mobile environments.
The experiments presented in this thesis were conducted by using user profile
datasets that reflect the user’s personal information, preferences and interests.
The simulations with existing classification and clustering algorithms (e.g. Bayesian Networks (BN), Naïve Bayesian (NB), Lazy learning of Bayesian
Rules (LBR), Iterative Dichotomister 3 (Id3)) were performed on the WEKA
(version 3.5.7) machine learning platform. WEKA serves as a workbench to
work with a collection of popular learning schemes implemented in JAVA. In
addition, the MDC-CC, MDC-BC and MDC-LZ have been implemented on
NetBeans IDE 6.1 Beta as a JAVA application and MATLAB. Finally, the real life
scenario is implemented as a Java Mobile Application (Java ME) on NetBeans
IDE 7.1. All simulation results were evaluated based on the error rate and
accuracy
Hybrid Recommender Systems: A Systematic Literature Review
Recommender systems are software tools used to generate and provide suggestions for items
and other entities to the users by exploiting various strategies. Hybrid recommender systems
combine two or more recommendation strategies in different ways to benefit from their complementary
advantages. This systematic literature review presents the state of the art in hybrid
recommender systems of the last decade. It is the first quantitative review work completely focused
in hybrid recommenders. We address the most relevant problems considered and present
the associated data mining and recommendation techniques used to overcome them. We also
explore the hybridization classes each hybrid recommender belongs to, the application domains,
the evaluation process and proposed future research directions. Based on our findings, most of
the studies combine collaborative filtering with another technique often in a weighted way. Also
cold-start and data sparsity are the two traditional and top problems being addressed in 23 and
22 studies each, while movies and movie datasets are still widely used by most of the authors.
As most of the studies are evaluated by comparisons with similar methods using accuracy metrics,
providing more credible and user oriented evaluations remains a typical challenge. Besides
this, newer challenges were also identified such as responding to the variation of user context,
evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid
recommenders represent a good basis with which to respond accordingly by exploring newer
opportunities such as contextualizing recommendations, involving parallel hybrid algorithms,
processing larger datasets, etc
Multicriteria Evaluation for Top-k and Sequence-based Recommender Systems
L'abstract è presente nell'allegato / the abstract is in the attachmen
Modeling User Preferences in Recommender Systems: A Classification Framework for Explicit and Implicit User Feedback
Recommender systems are firmly established as a standard technology for assisting users with their choices; however, little attention has been paid to the application of the user model in recommender systems, particularly the variability and noise that are an intrinsic part of human behavior and activity. To enable recommender systems to suggest items that are useful to a particular user, it can be essential to understand the user and his or her interactions with the system. These interactions typically manifest themselves as explicit and implicit user feedback that provides the key indicators for modeling users' preferences for items and essential information for personalizing recommendations. In this article, we propose a classification framework for the use of explicit and implicit user feedback in recommender systems based on a set of distinct properties that include Cognitive Effort, UserModel, Scale of Measurement, and Domain Relevance.We develop a set of comparison criteria for explicit and implicit user feedback to emphasize the key properties. Using our framework, we provide a classification of recommender systems that have addressed questions about user feedback, and we review state-of-the-art techniques to improve such user feedback and thereby improve the performance of the recommender system. Finally, we formulate challenges for future research on improvement of user feedback. © 2014 ACM
User Modeling and User Profiling: A Comprehensive Survey
The integration of artificial intelligence (AI) into daily life, particularly
through information retrieval and recommender systems, has necessitated
advanced user modeling and profiling techniques to deliver personalized
experiences. These techniques aim to construct accurate user representations
based on the rich amounts of data generated through interactions with these
systems. This paper presents a comprehensive survey of the current state,
evolution, and future directions of user modeling and profiling research. We
provide a historical overview, tracing the development from early stereotype
models to the latest deep learning techniques, and propose a novel taxonomy
that encompasses all active topics in this research area, including recent
trends. Our survey highlights the paradigm shifts towards more sophisticated
user profiling methods, emphasizing implicit data collection, multi-behavior
modeling, and the integration of graph data structures. We also address the
critical need for privacy-preserving techniques and the push towards
explainability and fairness in user modeling approaches. By examining the
definitions of core terminology, we aim to clarify ambiguities and foster a
clearer understanding of the field by proposing two novel encyclopedic
definitions of the main terms. Furthermore, we explore the application of user
modeling in various domains, such as fake news detection, cybersecurity, and
personalized education. This survey serves as a comprehensive resource for
researchers and practitioners, offering insights into the evolution of user
modeling and profiling and guiding the development of more personalized,
ethical, and effective AI systems.Comment: 71 page
Recommendation Systems: Decision Support for the Information Economy
Information Systems Working Papers Serie
Using implicit feedback for recommender systems: characteristics, applications, and challenges
Recommender systems are software tools to tackle the problem of information overload by helping users to find items that are most relevant for them within an often unmanageable set of choices. To create these personalized recommendations for a user, the algorithmic task of a recommender system is usually to quantify the user's interest in each item by predicting a relevance score, e.g., from the user's current situation or personal preferences in the past. Nowadays, recommender systems are used in various domains to recommend items such as products on e-commerce sites, movies and music on media portals, or people in social networks.
To assess the user's preferences, recommender systems proposed in past research often utilized explicit feedback, i.e., deliberately given ratings or like/dislike statements for items. In practice, however, in many of today's application domains of recommender systems this kind of information is not existent. Therefore, recommender systems have to rely on implicit feedback that is derived from the users' behavior and interactions with the system. This information can be extracted from navigation or transaction logs. Using implicit feedback leads to new challenges and open questions regarding, for example, the huge amount of signals to process, the ambiguity of the feedback, and the inevitable noise in the data. This thesis by publication explores some of these challenges and questions that have not been covered in previous research. The thesis is divided into two parts. In the first part, the thesis reviews existing works on implicit feedback and recommender systems that exploit these signals, especially in the Social Information Access domain, which utilizes the "community wisdom" of the social web for recommendations. Common application scenarios for implicit feedback are discussed and a categorization scheme that classifies different types of observable user behavior is established. In addition, state-of-the-art algorithmic approaches for implicit feedback are examined that, e.g., interpret implicit signals directly or convert them to explicit ratings to be able to use "classic" recommendation approaches that were designed for explicit feedback.
The second part of the thesis comprises some of the author's publications that deal with selected challenges of implicit feedback based recommendations. These contain (i) a specialized learning-to-rank algorithm that can differentiate different levels of interest indicator strength in implicit signals, (ii) contextualized recommendation techniques for the e-commerce domain that adapt product suggestions to customers' current short-term goals as well as their long-term preferences, and (iii) intelligent reminding approaches that aim at the re-discovery of relevant items in a customer's browsing history. Furthermore, the last paper of the thesis provides an in-depth analysis of different biases of various recommendation algorithms. Especially the popularity bias, the tendency to recommend mostly popular items, can be problematic in practical settings and countermeasures to reduce this bias are proposed
- …