scholarly journals Recommender Systems Based on Collaborative Filtering Using Review Texts—A Survey

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 317 ◽  
Author(s):  
Mehdi Srifi ◽  
Ahmed Oussous ◽  
Ayoub Ait Lahcen ◽  
Salma Mouline
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Information Source ◽  
Online Reviews ◽  
Filtering Algorithms ◽  
User Interests ◽  
Detailed Survey ◽  
Rich Information

In e-commerce websites and related micro-blogs, users supply online reviews expressing their preferences regarding various items. Such reviews are typically in the textual comments form, and account for a valuable information source about user interests. Recently, several works have used review texts and their related rich information like review words, review topics and review sentiments, for improving the rating-based collaborative filtering recommender systems. These works vary from one another on how they exploit the review texts for deriving user interests. This paper provides a detailed survey of recent works that integrate review texts and also discusses how these review texts are exploited for addressing some main issues of standard collaborative filtering algorithms.

Computing Recommendations with Collaborative Filtering

2011 ◽  
pp. 23-41
Author(s):  
Neal Lathia
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
System Research ◽  
Filtering Algorithms ◽  
Future Developments ◽  
The Past ◽  
Current Trends ◽  
The Future ◽  
The Way

Recommender systems generate personalized content for each of its users, by relying on an assumption reflected in the interaction between people: those who have had similar opinions in the past will continue sharing the same tastes in the future. Collaborative filtering, the dominant algorithm underlying recommender systems, uses a model of its users, contained within profiles, in order to guide what interactions should be allowed, and how these interactions translate first into predicted ratings, and then into recommendations. In this chapter, the authors introduce the various approaches that have been adopted when designing collaborative filtering algorithms, and how they differ from one another in the way they make use of the available user information. They then explore how these systems are evaluated, and highlight a number of problems that prevent recommendations from being suitably computed, before looking at the how current trends in recommender system research are projecting towards future developments.

Overview of Design Options for Neighborhood-Based Collaborative Filtering Systems

2011 ◽  
pp. 30-54 ◽  
Author(s):  
Nikos Manouselis ◽  
Constantina Costopoulou
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Design Stage ◽  
Filtering Algorithms ◽  
Related Literature ◽  
Design Options

The problem of collaborative filtering is to predict how well a user will like an item that he or she has not rated, given a set of historical ratings for this and other items from a community of users. A plethora of collaborative filtering algorithms have been proposed in related literature. One of the most prevalent families of collaborative filtering algorithms are neighborhood-based ones, which calculate a prediction of how much a user will like a particular item, based on how other users with similar preferences have rated this item. This chapter aims to provide an overview of various proposed design options for neighborhood-based collaborative filtering systems, in order to facilitate their better understanding, as well as their study and implementation by recommender systems’ researchers and developers. For this purpose, the chapter extends a series of design stages of neighborhood-based algorithms, as they have been initially identified by related literature on collaborative filtering systems. Then, it reviews proposed alternatives for each design stage and provides an overview of potential design options.

scholarly journals Learning from Online Voices: A Mixed Methods Approach to Explore Patient Online Reviews of Hospital Care in Portugal

2021 ◽  
Author(s):  
Carla Marisa Ferreira Gomes ◽  
Marlene Paula Castro Amorim ◽  
Mário Jorge Ferreira Rodrigues
Keyword(s):  
Hospital Care ◽  
Information Source ◽  
Healthcare Services ◽  
Online Reviews ◽  
Online Comments ◽  
Structured Analysis ◽  
Important Quality ◽  
Rich Information ◽  
The Right

Online patient reviews can offer a rich information source to users of healthcare services, as well as for hospital management and quality monitoring. Whereas in recent years the volume of online patient reviews has been consistently growing, organizations still lack standardized approaches and tools to allow for the systematic monitoring of users’ online comments. Therefore, managers are lagging in the ability to make use of such data from patients’ voices for improving the quality of the services provided. If organizations fail to develop the right capabilities to consider users’ online reviews and feedback, they risk not only to miss important quality failure alerts, as wells as to frustrate their customers’ expectations for service and attention. In this chapter, we present a qualitative analysis of patients’ reviews for healthcare services in Portugal, building on a sample of data extracted from Google for the year of 2019. The chapter reports the major quality management themes addressed by hospital users in their online expressions and offers some guidelines to support a structured analysis and visualization of results from online users’ word of mouth data.

Music Recommender Systems and Genre Bias

2013 ◽  
Author(s):  
Jason Neal
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Filtering Algorithms ◽  
Indexing And Retrieval ◽  
Music Recommender Systems ◽  
Per Se

Indexing and retrieval tools for music privilege genre-based categorization. Music recommender systems do not draw upon genre per se, but they utilize collaborative filtering algorithms that can give the appearance of doing so. This poster critiques genre’s pervasiveness, and suggests that recommender systems draw upon alternative notions of “similarity.”Les outils d’indexation et de repérage de la musique privilégient la catégorisation par genre. Les systèmes de recommandation de musique n’utilisent pas le genre en tant que tel, mais utilisent plutôt un algorithme de filtrage collaboratif qui en apparence fait la même chose. Cette affiche critique l’omniprésence du genre et suggère que les systèmes de recommandation devraient plutôt faire appel à la notion de « similarité ».

scholarly journals When Diversity Met Accuracy: A Story of Recommender Systems

Proceedings ◽  
2018 ◽  
Vol 2 (18) ◽  
pp. 1178 ◽  
Author(s):  
Alfonso Landin ◽  
Eva Suárez-García ◽  
Daniel Valcarce
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
The Other ◽  
Trade Off ◽  
Filtering Algorithms ◽  
Business Objectives

Diversity and accuracy are frequently considered as two irreconcilable goals in the field of Recommender Systems. In this paper, we study different approaches to recommendation, based on collaborative filtering, which intend to improve both sides of this trade-off. We performed a battery of experiments measuring precision, diversity and novelty on different algorithms. We show that some of these approaches are able to improve the results in all the metrics with respect to classical collaborative filtering algorithms, proving to be both more accurate and more diverse. Moreover, we show how some of these techniques can be tuned easily to favour one side of this trade-off over the other, based on user desires or business objectives, by simply adjusting some of their parameters.

Novel Approach against Reverse Bandwagon Profile Inject Attack in Recommender Systems

2013 ◽  
Vol 645 ◽  
pp. 247-250
Author(s):  
Xiao Hong Liao ◽  
Ping Hua Chen
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Filtering Algorithms ◽  
Attack Model ◽  
Novel Approach ◽  
Novel Method

Due to the simplicity and high recommending quality, collaborative filtering algorithms are the most successful recommender techniques and wildly used in e-commerce recommender systems. However, such systems are vulnerable to profile inject attack which is employed by inserting biased profiles into systems in order to influence the recommendations. In this paper, we propose a novel method against reverse bandwagon profile inject attack model. Our method is basing the standard collaborative filtering algorithms. Experiment results show that our method has better performance against reverse bandwagon attack model.

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