A study on multicriteria recommender system using implicit feedback and fuzzy linguistic approaches

2011 ◽  
Author(s):  
K. Palanivel ◽  
R. Sivakumar
Keyword(s):  
Recommender System ◽  
Implicit Feedback ◽  
Linguistic Approaches ◽  
Fuzzy Linguistic

scholarly journals Context-aware Music Recommender System Based on Implicit Feedback

2021 ◽  
Vol 36 (1) ◽  
pp. WI2-D_1-10
Author(s):  
Yasufumi Takama ◽  
Jing-cheng Zhang ◽  
Hiroki Shibata
Keyword(s):  
Recommender System ◽  
Implicit Feedback ◽  
Context Aware

scholarly journals METHOD FOR DETECTING SHILLING ATTACKS BASED ON IMPLICIT FEEDBACK IN RECOMMENDER SYSTEMS

2020 ◽  
Vol 5 ◽  
pp. 21-30
Author(s):  
Oksana Chala ◽  
Lyudmyla Novikova ◽  
Larysa Chernyshova ◽  
Angelika Kalnitskaya
Keyword(s):  
Recommender System ◽  
Input Data ◽  
Implicit Feedback ◽  
User Selection ◽  
Time Intervals ◽  
User Interests ◽  
Selection Processes ◽  
Key Characteristics ◽  
The Relationship ◽  
Explicit Feedback

The problem of identifying shilling attacks, which are aimed at forming false ratings of objects in the recommender system, is considered. The purpose of such attacks is to include in the recommended list of items the goods specified by the attacking user. The recommendations obtained as a result of the attack will not correspond to customers' real preferences, which can lead to distrust of the recommender system and a drop in sales. The existing methods for detecting shilling attacks use explicit feedback from the user and are focused primarily on building patterns that describe the key characteristics of the attack. However, such patterns only partially take into account the dynamics of user interests. A method for detecting shilling attacks using implicit feedback is proposed by comparing the temporal description of user selection processes and ratings. Models of such processes are formed using a set of weighted temporal rules that define the relationship in time between the moments when users select a given object. The method uses time-ordered input data. The method includes the stages of forming sets of weighted temporal rules for describing sales processes and creating ratings, calculating a set of ratings for these processes, and forming attack indicators based on a comparison of the ratings obtained. The resulting signs make it possible to distinguish between nuke and push attacks. The method is designed to identify discrepancies in the dynamics of purchases and ratings, even in the absence of rating values at certain time intervals. The technique makes it possible to identify an approach to masking an attack based on a comparison of the rating values and the received attack indicators. When applied iteratively, the method allows to refine the list of profiles of potential attackers. The technique can be used in conjunction with pattern-oriented approaches to identifying shilling attacks

A recommender system for research resources based on fuzzy linguistic modeling

2009 ◽  
Vol 36 (3) ◽  
pp. 5173-5183 ◽  
Author(s):  
C. Porcel ◽  
A.G. López-Herrera ◽  
E. Herrera-Viedma
Keyword(s):  
Recommender System ◽  
Linguistic Modeling ◽  
Fuzzy Linguistic ◽  
Research Resources

scholarly journals Re-Enrichment Learning: Metadata Saliency for the Evolutive Personalization of a Recommender System

2021 ◽  
Vol 11 (4) ◽  
pp. 1733
Author(s):  
Yuseok Ban ◽  
Kyungjae Lee
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Experimental Results ◽  
Implicit Feedback ◽  
Digital Platforms ◽  
Novel Approach ◽  
Domain Transfer ◽  
Explicit Feedback

Many studies have been conducted on recommender systems in both the academic and industrial fields, as they are currently broadly used in various digital platforms to make personalized suggestions. Despite the improvement in the accuracy of recommenders, the diversity of interest areas recommended to a user tends to be reduced, and the sparsity of explicit feedback from users has been an important issue for making progress in recommender systems. In this paper, we introduce a novel approach, namely re-enrichment learning, which effectively leverages the implicit logged feedback from users to enhance user retention in a platform by enriching their interest areas. The approach consists of (i) graph-based domain transfer and (ii) metadata saliency, which (i) find an adaptive and collaborative domain representing the relations among many users’ metadata and (ii) extract attentional features from a user’s implicit logged feedback, respectively. The experimental results show that our proposed approach has a better capacity to enrich the diversity of interests of a user by means of implicit feedback and to help recommender systems achieve more balanced personalization. Our approach, finally, helps recommenders improve user retention, i.e., encouraging users to click more items or dwell longer on the platform.

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