Use Of Collaborative Filtering Research Articles

Measuring the effect of collaborative filtering on the diversity of users’ attention

While the ever-increasing emergence of online services has led to a growing interest in the development of recommender systems, the algorithms underpinning such systems have begun to be criticized for their role in limiting the variety of content exposed to users. In this context, the notion of diversity has been proposed as a way of mitigating the side effects resulting from the specialization of recommender systems. In this paper, using a well-known recommender system that makes use of collaborative filtering in the context of musical content, we analyze the diversity of recommendations generated through the lens of the recently proposed information network diversity measure. The results of our study offer significant insights into the effect of algorithmic recommendations. On the one hand, we show that the musical selections of a large proportion of users are diversified as a result of the recommendations. On the other hand, however, such improvements do not benefit all users. They are in fact mainly restricted to users with a low level of activity or whose past musical listening selections are very narrow. Through more in-depth investigations, we also discovered that while recommendations generally increase the variety of the songs recommended to users, they nonetheless fail to provide a balanced exposure to the different related categories.

Application of recommendation system in educational Field

The article is devoted to the issue of the application of recommender system technology in the educational sphere. It was determined that certain tasks of the educational field can be described in terms of recommendation systems: the user who is given a recommendation is the education applicant; the objects of his interest are disciplines from the catalog of optional disciplines, sources from the library of the educational institution, topics (or research area) of qualification papers, potential supervisors of qualification papers, external courses for the organization of self-education. A list of resources was created to determine the characteristics of the education applicants’ objects of interest and the education applicants themselves within the framework of formal education, and an analysis of their content and method of formation was carried out. The structure of the recommendation system has been developed; its feature is that each object of interest of the education applicant corresponds to a separate module of recommendations. At the same time, the recommendation modules use shared resources, including databases of the educational institution with direct information about the objects of interest and the education applicant, as well as survey databases consisting of general regular mandatory surveys for education applicants at the level of the educational institution and contextual surveys regarding objects of interest. A hybrid approach was chosen to form the recommendations. The structure of the system provides for the use of collaborative filtering for processing survey results, and data presented in the form of semi-structured or arbitrary texts in natural language is proposed to be processed using Natural Language Processing and Text Mining methods, which will allow taking into account semantically similar values of the characteristics of objects of interest and users and thus to improve the quality of the recommendation system.

Improving the web-based recommender system through collaborative filtering

The solution to the problem of improving the recommendation web-oriented system through the use of collaborative filtering to provide recommendations to users of the web service with articles on gaming topics is presented. In the course of the research, a set of stages of development and improvement of the recommendatory web-oriented system was systematized. The structure of the database and the method of collaborative filtering using the proposed similarity measure to form recommendations for users are presented. The preliminary structuring of articles by categories was used to identify user preferences and provide recommendations. The use of collaborative filtering with the proposed similarity measure allowed us to increase the number of articles viewed by users. Ill.: 2. Bibliography: 10 titles.

Combining Classification and User-Based Collaborative Filtering for Matching Footwear Size

Size mismatch is a serious problem in online footwear purchase because size mismatch implies an almost sure return. Not only foot measurements are important in selecting a size, but also user preference. This is the reason we propose several methodologies that combine the information provided by a classifier with anthropometric measurements and user preference information through user-based collaborative filtering. As novelties: (1) the information sources are 3D foot measurements from a low-cost 3D foot digitizer, past purchases and self-reported size; (2) we propose to use an ordinal classifier after imputing missing data with different options based on the use of collaborative filtering; (3) we also propose an ensemble of ordinal classification and collaborative filtering results; and (4) several methodologies based on clustering and archetype analysis are introduced as user-based collaborative filtering for the first time. The hybrid methodologies were tested in a simulation study, and they were also applied to a dataset of Spanish footwear users. The results show that combining the information from both sources predicts the foot size better and the new proposals provide better accuracy than the classic alternatives considered.

Hybrid system for video game recommendation based on implicit ratings and social networks

The digital entertainment sector is one of the fastest growing in recent years. In the case of video games, the productions of some of the most popular titles are on a par with film productions. The sale of video games is in the millions, and yet there are few works on the recommendation of video games. In this work a hybrid system of video game recommendation is presented, through the use of collaborative filtering and content-based filtering, and the construction of relationship graphs. In order to improve the recommendations, a new method for estimating implicit ratings is proposed that takes into account the hours of play. The proposed recommender system improves the results of other techniques presented in the state of the art.

Privacy-preserving collaborative recommendations based on random perturbations

Collaborative recommender systems offer a solution to the information overload problem found in online environments such as e-commerce. The use of collaborative filtering, the most widely used recommendation method, gives rise to potential privacy issues. In addition, the user ratings utilized in collaborative filtering systems to recommend products or services must be protected. The purpose of this research is to provide a solution to the privacy concerns of collaborative filtering users, while maintaining high accuracy of recommendations. This paper proposes a multi-level privacy-preserving method for collaborative filtering systems by perturbing each rating before it is submitted to the server. The perturbation method is based on multiple levels and different ranges of random values for each level. Before the submission of each rating, the privacy level and the perturbation range are selected randomly from a fixed range of privacy levels. The proposed privacy method has been experimentally evaluated with the results showing that with a small decrease of utility, user privacy can be protected, while the proposed approach offers practical and effective results.

A comprehensive evaluation of scholarly paper recommendation using potential citation papers

To help generate relevant suggestions for researchers, recommendation systems have started to leverage the latent interests in the publication profiles of the researchers themselves. While using such a publication citation network has been shown to enhance performance, the network is often sparse, making recommendation difficult. To alleviate this sparsity, in our former work, we identified "potential citation papers" through the use of collaborative filtering. Also, as different logical sections of a paper have different significance, as a secondary contribution, we investigated which sections of papers can be leveraged to represent papers effectively. While this initial approach works well for researchers vested in a single discipline, it generates poor predictions for scientists who work on several different topics in the discipline (hereafter, "intra-disciplinary"). We thus extend our previous work in this paper by proposing an adaptive neighbor selection method to overcome this problem in our imputation-based collaborative filtering framework. On a publicly-available scholarly paper recommendation dataset, we show that recommendation accuracy significantly outperforms state-of-the-art recommendation baselines as measured by nDCG and MRR, when using our adaptive neighbor selection method. While recommendation performance is enhanced for all researchers, improvements are more marked for intra-disciplinary researchers, showing that our method does address the targeted audience.

How the digital sales professional will change marketing’s customer intelligence

Leading-edge personalisation companies like Amazon and Netflix pioneered the use of collaborative filtering to convert customer similarities into powerful predictors of interests. Many organisations followed suit, and big data arose in part with this objective in mind. Yet sales organisations have lagged in this area, with limited experimentation with statistically-based optimisation solutions. More importantly, sales and marketing organisations have largely failed to unify their customer data to make this possible. However, change is in the making. As sales organisations adopt intelligent mobile solutions in their engagements with customers, the opportunity to track receptiveness at a granular level is bringing a volume of preference data to the enterprise, similar to how marketing data exploded with the advent of the internet. This paper addresses how intelligent mobile applications have created the digital sales professional, and how this is changing companies’ abilities to share customer data throughout their organisations. This is enabling an optimal and consistent approach by sales and marketing organisations, and a much improved experience for the customer.

Manifesto for the Reputation Society

Information overload, challenges of evaluating quality, and the opportunity to benefit from experiences of others have spurred the development of reputation systems. Most Internet sites which mediate between large numbers of people use some form of reputation mechanism: Slashdot, eBay, ePinions, Amazon, and Google all make use of collaborative filtering, recommender systems, or shared judgements of quality. But we suggest the potential utility of reputation services is far greater, touching nearly every aspect of society. By leveraging our limited and local human judgement power with collective networked filtering, it is possible to promote an interconnected ecology of socially beneficial reputation systems — to restrain the baser side of human nature, while unleashing positive social changes and enabling the realization of ever higher goals.

Creating user-adapted Websites by the use of collaborative filtering

The information globalization induced by the rapid development of the Internet and the accompanying adoption of the Web throughout the society leads to Websites which reach large audiences. The diversity of the audiences and the need of customer retention require active Websites, which expose themselves in a customized or personalized way: We call those sites User-adapted Websites. New technologies are necessary to personalize and customize content. Information filtering can be used for the discovery of important content and is therefore a key-technology for the creation of user-adapted Websites. In this article, we focus on the application of collaborative filtering for user-adapted Websites. We studied techniques for combining and integrating content-based filtering with collaborative filtering in order to address typical problems in collaborative filtering systems and to improve the performance. Other issues which are mentioned but only lightly covered include user interface challenges. To validate our approaches we developed a prototype user-adapted Website, the Active WebMuseum, a museum Website, which exposes its collection in a personalized way by the use of collaborative filtering.