421 research outputs found
Utilizing implicit feedback data to build a hybrid recommender system
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsIn e-commerce applications, buyers are overwhelmed by the number of products due to
the high depth of assortments. They may be interested in receiving recommendations
to assist with their purchasing decisions. However, many recommendation engines
perform poorly in the absence of community data and contextual data. This thesis
examines a hybrid matrix factorisation model, LightFM, representing users and items
as linear combinations of their content featuresā latent factors. The model embedding
item features displays superior user and item cold-start performance. The results
demonstrate the importance of selectively embedding contextual data in the presence
of cold-start
The state-of-the-art in personalized recommender systems for social networking
With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match usersā personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0
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. Ā© 2014 ACM
Enhancing Job Recommendation through LLM-based Generative Adversarial Networks
Recommending suitable jobs to users is a critical task in online recruitment
platforms, as it can enhance users' satisfaction and the platforms'
profitability. While existing job recommendation methods encounter challenges
such as the low quality of users' resumes, which hampers their accuracy and
practical effectiveness. With the rapid development of large language models
(LLMs), utilizing the rich external knowledge encapsulated within them, as well
as their powerful capabilities of text processing and reasoning, is a promising
way to complete users' resumes for more accurate recommendations. However,
directly leveraging LLMs to enhance recommendation results is not a
one-size-fits-all solution, as LLMs may suffer from fabricated generation and
few-shot problems, which degrade the quality of resume completion. In this
paper, we propose a novel LLM-based approach for job recommendation. To
alleviate the limitation of fabricated generation for LLMs, we extract accurate
and valuable information beyond users' self-description, which helps the LLMs
better profile users for resume completion. Specifically, we not only extract
users' explicit properties (e.g., skills, interests) from their
self-description but also infer users' implicit characteristics from their
behaviors for more accurate and meaningful resume completion. Nevertheless,
some users still suffer from few-shot problems, which arise due to scarce
interaction records, leading to limited guidance for the models in generating
high-quality resumes. To address this issue, we propose aligning unpaired
low-quality with high-quality generated resumes by Generative Adversarial
Networks (GANs), which can refine the resume representations for better
recommendation results. Extensive experiments on three large real-world
recruitment datasets demonstrate the effectiveness of our proposed method.Comment: 13 pages, 6 figures, 3 table
A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product Recommendation
E-commerce recommendation systems facilitate customersā purchase decision by recommending products or services of interest (e.g., Amazon). Designing a recommender system tailored toward an individual customerās need is crucial for retailers to increase revenue and retain customersā loyalty. As usersā interests and preferences change with time, the time stamp of a user interaction (click, view or purchase event) is an important characteristic to learn sequential patterns from these user interactions and, hence, understand usersā long- and short-term preferences to predict the next item(s) for recommendation. This paper presents a taxonomy of sequential recommendation systems (SRecSys) with a focus on e-commerce product recommendation as an application and classifies SRecSys under three main categories as: (i) traditional approaches (sequence similarity, frequent pattern mining and sequential pattern mining), (ii) factorization and latent representation (matrix factorization and Markov models) and (iii) neural network-based approaches (deep neural networks, advanced models). This classification contributes towards enhancing the understanding of existing SRecSys in the literature with the application domain of e-commerce product recommendation and provides current status of the solutions available alongwith future research directions. Furthermore, a classification of surveyed systems according to eight important key features supported by the techniques along with their limitations is also presented. A comparative performance analysis of the presented SRecSys based on experiments performed on e-commerce data sets (Amazon and Online Retail) showed that integrating sequential purchase patterns into the recommendation process and modeling usersā sequential behavior improves the quality of recommendations
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