1,789 research outputs found
Utilizing Alike Neighbor Influenced Similarity Metric for Efficient Prediction in Collaborative Filter-Approach-Based Recommendation System
The most popular method collaborative filter approach is primarily used to handle the
information overloading problem in E-Commerce. Traditionally, collaborative filtering uses ratings
of similar users for predicting the target item. Similarity calculation in the sparse dataset greatly
influences the predicted rating, as less count of co-rated items may degrade the performance of the
collaborative filtering. However, consideration of item features to find the nearest neighbor can
be a more judicious approach to increase the proportion of similar users. In this study, we offer a
new paradigm for raising the rating prediction accuracy in collaborative filtering. The proposed
framework uses rated items of the similar feature of the ’most’ similar individuals, instead of using the
wisdom of the crowd. The reliability of the proposed framework is evaluated on the static MovieLens
datasets and the experimental results corroborate our anticipations
Hybrid Temporal Dynamics Feature Extraction in Recommendation Systems for Improved Ranking of Items
In today's retail landscape, shopping malls and e-commerce platforms employ various psychological tactics to influence customer behavior and increase profits. In line with these strategies, this paper introduces an innovative method for recognizing sentiment patterns, with a specific emphasis on the evolving temporal aspects of user interests within Recommendation Systems (RS). The projected method, called Temporal Dynamic Features based User Sentiment Pattern for Recommendation System (TDF-USPRS), aims to enhance the performance of RS by leveraging sentiment trends derived from a user's past preferences. TDF-USPRS utilizes a hybrid model combining Short Time Fourier Transform (STFT) and a layered architecture based on Bidirectional Long Short-Term Memory (BiLSTM) to retrieve temporal dynamics and discern a user's sentiment trend. Through an examination of a user's sequential history of item preferences, TDF-USPRS produces sentiment patterns to offer exceptionally pertinent recommendations, even in cases of sparse datasets. A variety of popular datasets, including as MovieLens, Amazon Rating Beauty, YOOCHOOSE, and CiaoDVD are utilised to assess the suggested technique. The TDF-USPRS model outperforms existing approaches, according to experimental data, resulting in recommendations with greater accuracy and relevance. Comparing the projected model to existing approaches, the projected model displays a 6.5% reduction in RMSE and a 4.5% gain in precision. Specifically, the model achieves an RMSE of 0.7623 and 0.996 on the MovieLens and CiaoDVD datasets, while attaining a precision score of 0.5963 and 0.165 on the YOOCHOOSE and Amazon datasets, respectively
Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review
A filter bubble refers to the phenomenon where Internet customization
effectively isolates individuals from diverse opinions or materials, resulting
in their exposure to only a select set of content. This can lead to the
reinforcement of existing attitudes, beliefs, or conditions. In this study, our
primary focus is to investigate the impact of filter bubbles in recommender
systems. This pioneering research aims to uncover the reasons behind this
problem, explore potential solutions, and propose an integrated tool to help
users avoid filter bubbles in recommender systems. To achieve this objective,
we conduct a systematic literature review on the topic of filter bubbles in
recommender systems. The reviewed articles are carefully analyzed and
classified, providing valuable insights that inform the development of an
integrated approach. Notably, our review reveals evidence of filter bubbles in
recommendation systems, highlighting several biases that contribute to their
existence. Moreover, we propose mechanisms to mitigate the impact of filter
bubbles and demonstrate that incorporating diversity into recommendations can
potentially help alleviate this issue. The findings of this timely review will
serve as a benchmark for researchers working in interdisciplinary fields such
as privacy, artificial intelligence ethics, and recommendation systems.
Furthermore, it will open new avenues for future research in related domains,
prompting further exploration and advancement in this critical area.Comment: 21 pages, 10 figures and 5 table
A scalable recommender system : using latent topics and alternating least squares techniques
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsA recommender system is one of the major techniques that handles information overload problem of Information Retrieval. Improves access and proactively recommends relevant information to each user, based on preferences and objectives. During the implementation and planning phases, designers have to cope with several issues and challenges that need proper attention. This thesis aims to show the issues and challenges in developing high-quality recommender systems.
A paper solves a current research problem in the field of job recommendations using a distributed algorithmic framework built on top of Spark for parallel computation which allows the algorithm to scale linearly with the growing number of users.
The final solution consists of two different recommenders which could be utilised for different purposes. The first method is mainly driven by latent topics among users, meanwhile the second technique utilises a latent factor algorithm that directly addresses the preference-confidence paradigm
Understanding and Mitigating Multi-sided Exposure Bias in Recommender Systems
Fairness is a critical system-level objective in recommender systems that has
been the subject of extensive recent research. It is especially important in
multi-sided recommendation platforms where it may be crucial to optimize
utilities not just for the end user, but also for other actors such as item
sellers or producers who desire a fair representation of their items. Existing
solutions do not properly address various aspects of multi-sided fairness in
recommendations as they may either solely have one-sided view (i.e. improving
the fairness only for one side), or do not appropriately measure the fairness
for each actor involved in the system. In this thesis, I aim at first
investigating the impact of unfair recommendations on the system and how these
unfair recommendations can negatively affect major actors in the system. Then,
I seek to propose solutions to tackle the unfairness of recommendations. I
propose a rating transformation technique that works as a pre-processing step
before building the recommendation model to alleviate the inherent popularity
bias in the input data and consequently to mitigate the exposure unfairness for
items and suppliers in the recommendation lists. Also, as another solution, I
propose a general graph-based solution that works as a post-processing approach
after recommendation generation for mitigating the multi-sided exposure bias in
the recommendation results. For evaluation, I introduce several metrics for
measuring the exposure fairness for items and suppliers, and show that these
metrics better capture the fairness properties in the recommendation results. I
perform extensive experiments to evaluate the effectiveness of the proposed
solutions. The experiments on different publicly-available datasets and
comparison with various baselines confirm the superiority of the proposed
solutions in improving the exposure fairness for items and suppliers.Comment: Doctoral thesi
EvoRecSys: Evolutionary framework for health and well-being recommender systems
Hugo Alcaraz-Herrera's PhD is supported by The Mexican Council of Science and Technology (Consejo Nacional de Ciencia y Tecnologia - CONACyT).In recent years, recommender systems have been employed in domains like ecommerce,
tourism, and multimedia streaming, where personalising users’ experience
based on their interactions is a fundamental aspect to consider. Recent recommender
system developments have also focused on well-being, yet existing solutions have
been entirely designed considering one single well-being aspect in isolation, such
as a healthy diet or an active lifestyle. This research introduces EvoRecSys, a novel
recommendation framework that proposes evolutionary algorithms as the main recommendation
engine, thereby modelling the problem of generating personalised
well-being recommendations as a multi-objective optimisation problem. EvoRecSys
captures the interrelation between multiple aspects of well-being by constructing configurable
recommendations in the form of bundled items with dynamic properties. The preferences and a predefined well-being goal by the user are jointly considered.
By instantiating the framework into an implemented model, we illustrate the use of
a genetic algorithm as the recommendation engine. Finally, this implementation has
been deployed as a Web application in order to conduct a users’ study.Consejo Nacional de Ciencia y Tecnologia (CONACyT
Contextual Social Networking
The thesis centers around the multi-faceted research question of how contexts may
be detected and derived that can be used for new context aware Social Networking
services and for improving the usefulness of existing Social Networking services, giving
rise to the notion of Contextual Social Networking. In a first foundational part,
we characterize the closely related fields of Contextual-, Mobile-, and Decentralized
Social Networking using different methods and focusing on different detailed
aspects. A second part focuses on the question of how short-term and long-term
social contexts as especially interesting forms of context for Social Networking may
be derived. We focus on NLP based methods for the characterization of social relations
as a typical form of long-term social contexts and on Mobile Social Signal
Processing methods for deriving short-term social contexts on the basis of geometry
of interaction and audio. We furthermore investigate, how personal social agents
may combine such social context elements on various levels of abstraction. The third
part discusses new and improved context aware Social Networking service concepts.
We investigate special forms of awareness services, new forms of social information
retrieval, social recommender systems, context aware privacy concepts and services
and platforms supporting Open Innovation and creative processes.
This version of the thesis does not contain the included publications because of
copyrights of the journals etc. Contact in terms of the version with all included
publications: Georg Groh, [email protected] zentrale Gegenstand der vorliegenden Arbeit ist die vielschichtige Frage, wie Kontexte detektiert und abgeleitet werden können, die dazu dienen können, neuartige kontextbewusste Social Networking Dienste zu schaffen und bestehende Dienste in ihrem Nutzwert zu verbessern. Die (noch nicht abgeschlossene) erfolgreiche Umsetzung dieses Programmes führt auf ein Konzept, das man als Contextual Social Networking bezeichnen kann. In einem grundlegenden ersten Teil werden die eng zusammenhängenden Gebiete Contextual Social Networking, Mobile Social Networking und Decentralized Social Networking mit verschiedenen Methoden und unter Fokussierung auf verschiedene Detail-Aspekte näher beleuchtet und in Zusammenhang gesetzt. Ein zweiter Teil behandelt die Frage, wie soziale Kurzzeit- und Langzeit-Kontexte als für das Social Networking besonders interessante Formen von Kontext gemessen und abgeleitet werden können. Ein Fokus liegt hierbei auf NLP Methoden zur Charakterisierung sozialer Beziehungen als einer typischen Form von sozialem Langzeit-Kontext. Ein weiterer Schwerpunkt liegt auf Methoden aus dem Mobile Social Signal Processing zur Ableitung sinnvoller sozialer Kurzzeit-Kontexte auf der Basis von Interaktionsgeometrien und Audio-Daten. Es wird ferner untersucht, wie persönliche soziale Agenten Kontext-Elemente verschiedener Abstraktionsgrade miteinander kombinieren können. Der dritte Teil behandelt neuartige und verbesserte Konzepte für kontextbewusste Social Networking Dienste. Es werden spezielle Formen von Awareness Diensten, neue Formen von sozialem Information Retrieval, Konzepte für kontextbewusstes Privacy Management und Dienste und Plattformen zur Unterstützung von Open Innovation und Kreativität untersucht und vorgestellt. Diese Version der Habilitationsschrift enthält die inkludierten Publikationen zurVermeidung von Copyright-Verletzungen auf Seiten der Journals u.a. nicht. Kontakt in Bezug auf die Version mit allen inkludierten Publikationen: Georg Groh, [email protected]
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