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.

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

Clustering Approaches for Top-k Recommender Systems

2019 ◽  
Vol 28 (05) ◽  
pp. 1950019 ◽  
Author(s):  
Nicolás Torres ◽  
Marcelo Mendoza
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Large Scale ◽  
Empirical Studies ◽  
Cluster Structure ◽  
Parallel Architecture ◽  
Evaluation Framework ◽  
Training Data ◽  
Cluster Solution ◽  
Novel Approach

Clustering-based recommender systems bound the seek of similar users within small user clusters providing fast recommendations in large-scale datasets. Then groups can naturally be distributed into different data partitions scaling up in the number of users the recommender system can handle. Unfortunately, while the number of users and items included in a cluster solution increases, the performance in terms of precision of a clustering-based recommender system decreases. We present a novel approach that introduces a cluster-based distance function used for neighborhood computation. In our approach, clusters generated from the training data provide the basis for neighborhood selection. Then, to expand the search of relevant users, we use a novel measure that can exploit the global cluster structure to infer cluster-outside user’s distances. Empirical studies on five widely known benchmark datasets show that our proposal is very competitive in terms of precision, recall, and NDCG. However, the strongest point of our method relies on scalability, reaching speedups of 20× in a sequential computing evaluation framework and up to 100× in a parallel architecture. These results show that an efficient implementation of our cluster-based CF method can handle very large datasets providing also good results in terms of precision, avoiding the high computational costs involved in the application of more sophisticated techniques.

scholarly journals Keywords-Driven and Popularity-Aware Paper Recommendation Based on Undirected Paper Citation Graph

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Hanwen Liu ◽  
Huaizhen Kou ◽  
Chao Yan ◽  
Lianyong Qi
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Large Scale ◽  
Real Life ◽  
Research Field ◽  
Experimental Results ◽  
The Real ◽  
Matching Technique ◽  
Paper Citation ◽  
Citation Graph

Nowadays, scholar recommender systems often recommend academic papers based on users’ personalized retrieval demands. Typically, a recommender system analyzes the keywords typed by a user and then returns his or her preferred papers, in an efficient and economic manner. In practice, one paper often contains partial keywords that a user is interested in. Therefore, the recommender system needs to return the user a set of papers that collectively covers all the queried keywords. However, existing recommender systems only use the exact keyword matching technique for recommendation decisions, while neglecting the correlation relationships among different papers. As a consequence, it may output a set of papers from multiple disciplines that are different from the user’s real research field. In view of this shortcoming, we propose a keyword-driven and popularity-aware paper recommendation approach based on an undirected paper citation graph, named PRkeyword+pop. At last, we conduct large-scale experiments on the real-life Hep-Th dataset to further demonstrate the usefulness and feasibility of PRkeyword+pop. Experimental results prove the advantages of PRkeyword+pop in searching for a set of satisfactory papers compared with other competitive approaches.

scholarly journals Session-Based Recommender System for Sustainable Digital Marketing

2019 ◽  
Vol 11 (12) ◽  
pp. 3336 ◽  
Author(s):  
Hyunwoo Hwangbo ◽  
Yangsok Kim
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
High Performance ◽  
Marketing Strategies ◽  
Feature Weighting ◽  
Experimental Results ◽  
Digital Marketing ◽  
Chi Square ◽  
Fashion Products

Many companies operate e-commerce websites to sell fashion products. Some customers want to buy products with intention of sustainability and therefore the companies need to suggest appropriate fashion products to those customers. Recommender systems are key applications in these sustainable digital marketing strategies and high performance is the most necessary factor. This research aims to improve recommendation systems’ performance by considering item session and attribute session information. We suggest the Item Session-Based Recommender (ISBR) and the Attribute Session-Based Recommenders (ASBRs) that use item and attribute session data independently, and then we suggest the Feature-Weighted Session-Based Recommenders (FWSBRs) that combine multiple ASBRs with various feature weighting schemes. Our experimental results show that FWSBR with chi-square feature weighting scheme outperforms ISBR, ASBRs, and Collaborative Filtering Recommender (CFR). In addition, it is notable that FWSBRs overcome the cold-start item problem, one significant limitation of CFR and ISBR, without losing performance.

scholarly journals An Effective e-Commerce Recommender System Based on Trust and Semantic Information

2021 ◽  
Vol 21 (1) ◽  
pp. 103-118
Author(s):  
Qusai Y. Shambour ◽  
Nidal M. Turab ◽  
Omar Y. Adwan
Keyword(s):  
Electronic Commerce ◽  
Recommender Systems ◽  
Recommender System ◽  
Semantic Information ◽  
Significant Rise ◽  
Experimental Results ◽  
Semantic Relations ◽  
The Internet ◽  
Rating Data ◽  
Recommendation Accuracy

Abstract Electronic commerce has been growing gradually over the last decade as a new driver of the retail industry. In fact, the growth of e-Commerce has caused a significant rise in the number of choices of products and services offered on the Internet. This is where recommender systems come into play by providing meaningful recommendations to consumers based on their needs and interests effectively. However, recommender systems are still vulnerable to the scenarios of sparse rating data and cold start users and items. To develop an effective e-Commerce recommender system that addresses these limitations, we propose a Trust-Semantic enhanced Multi-Criteria CF (TSeMCCF) approach that exploits the trust relations and multi-criteria ratings of users, and the semantic relations of items within the CF framework to achieve effective results when sufficient rating data are not available. The experimental results have shown that the proposed approach outperforms other benchmark recommendation approaches with regard to recommendation accuracy and coverage.

scholarly journals Integrating selection-based aspect sentiment and preference knowledge for social recommender systems

2019 ◽  
Vol 44 (2) ◽  
pp. 399-416
Author(s):  
Yoke Yie Chen ◽  
Nirmalie Wiratunga ◽  
Robert Lothian
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Explicit Knowledge ◽  
State Of The Art ◽  
Implicit Knowledge ◽  
Feature Weighting ◽  
Product Reviews ◽  
Content Type ◽  
Product Categories ◽  
Novel Approach

Purpose Recommender system approaches such as collaborative and content-based filtering rely on user ratings and product descriptions to recommend products. More recently, recommender system research has focussed on exploiting knowledge from user-generated content such as product reviews to enhance recommendation performance. The purpose of this paper is to show that the performance of a recommender system can be enhanced by integrating explicit knowledge extracted from product reviews with implicit knowledge extracted from analysis of consumer’s purchase behaviour. Design/methodology/approach The authors introduce a sentiment and preference-guided strategy for product recommendation by integrating not only explicit, user-generated and sentiment-rich content but also implicit knowledge gleaned from users’ product purchase preferences. Integration of both of these knowledge sources helps to model sentiment over a set of product aspects. The authors show how established dimensionality reduction and feature weighting approaches from text classification can be adopted to weight and select an optimal subset of aspects for recommendation tasks. The authors compare the proposed approach against several baseline methods as well as the state-of-the-art better method, which recommends products that are superior to a query product. Findings Evaluation results from seven different product categories show that aspect weighting and selection significantly improves state-of-the-art recommendation approaches. Research limitations/implications The proposed approach recommends products by analysing user sentiment on product aspects. Therefore, the proposed approach can be used to develop recommender systems that can explain to users why a product is recommended. This is achieved by presenting an analysis of sentiment distribution over individual aspects that describe a given product. Originality/value This paper describes a novel approach to integrate consumer purchase behaviour analysis and aspect-level sentiment analysis to enhance recommendation. In particular, the authors introduce the idea of aspect weighting and selection to help users identify better products. Furthermore, the authors demonstrate the practical benefits of this approach on a variety of product categories and compare the approach with the current state-of-the-art approaches.

Utilizing Association Rules for Improving the Performance of Collaborative Filtering

2012 ◽  
Vol 3 (2) ◽  
pp. 14-28 ◽  
Author(s):  
Zainab Khanzadeh ◽  
Mehregan Mahdavi
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Association Rules ◽  
Cold Start ◽  
User Preferences ◽  
Experimental Results ◽  
Internet Technology ◽  
Promising Technique ◽  
Novel Approach ◽  
Better Than

Internet technology has rapidly grown during the last decades. Presently, users are faced with a great amount of information and they need help to find appropriate items in the shortest possible time. Recommender systems were introduced to overcome this problem of overloaded information. They recommend items of interest to users based on their expressed preferences. Major e-commerce companies try to use this technology to increase their sales. Collaborative Filtering is the most promising technique in recommender systems. It provides personalized recommendations according to user preferences. But one of the problems of Collaborative Filtering is cold-start. The authors provide a novel approach for solving this problem through using the attributes of items in order to recommend items to more people for improving e-business activities. The experimental results show that the proposed method performs better than existing methods in terms of the number of generated recommendations and their quality.

scholarly journals Social Media Data Analysis using Recommendation Algorithms

2019 ◽  
Vol 8 (12S) ◽  
pp. 394-397
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Recommender System ◽  
User Preferences ◽  
Implicit Feedback ◽  
Social Media Data ◽  
Recommendation Algorithms ◽  
The Subject ◽  
Media Data ◽  
Explicit Feedback

Recommender frameworks (RSs) are utilized in application areas to help clients in the quest for their preferred items .Recommender system filters information which takes users ratings and predict user preferences in ecommerce and other categorical websites. We examine individual proposal dependent on client inclinations and search the neighbors through the client inclinations. It generates recommendations based on implicit feedback or explicit feedback. Implicit feedback is based on analysis of browsing patterns of the user. Express criticism is produced from the appraisals given by the client. All the more extensively tended to was the subject of AI's calculations, centered around separating calculations dependent on the clients or questions, and dependent on substance.

scholarly journals Critique Generation to Increase Diversity in Conversational Recipe Recommender System

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Fakhri G Abbas ◽  
Nadia Najjar ◽  
David Wilson
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Search Space ◽  
Initial Evaluation ◽  
Exploration Process ◽  
Food Items ◽  
Novel Approach ◽  
Nutritional Health ◽  
Conversational Interactions

Conversational recommender systems help to guide users in exploring the search space in order to discover items of interest. During the exploration process, the user provides feedback on recommended items to refine subsequent recommendations. Critiquing as a way of feedback has proven effective for conversational interactions. In addition, diversifying the recommended items during exploration can help to increase user understanding of the search space, which critiquing alone will not achieve. Both aspects are important elements for recommender applications in the food domain. Diversity in diet has been shown to predict nutritional health, and conversational exploration can help to introduce new food items. In this paper, we introduce a novel approach that brings together critique and diversity to support conversational recommendation in the recipe domain. Initial evaluation in comparison to a baseline similarity-based recommender shows that the proposed approach increases diversity during the exploration process.

scholarly journals Sistema de recomendación de contenidos para libros electrónicos inteligentes [Contents recommender system for intelligent eBooks]

2012 ◽  
Author(s):  
Edward Rolando Núnez Valdez ◽  
Vicente García Díaz ◽  
Jordan Pascual Espada ◽  
Carlos Enrique Montenegro Marín ◽  
Juan Manuel Cueva Lovelle ◽  
...  
Keyword(s):  
Social Network ◽  
Recommender System ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Implicit Feedback ◽  
Electronic Books ◽  
Collective Knowledge ◽  
Content Recommendation ◽  
Explicit Feedback

Resumen Un sistema de recomendación de contenidos para libros electrónicos inteligentes permite construir conocimientos colectivos para un conjunto de usuarios de una red social. Basándose en el análisis del comportamiento, preferencias y antecedentes de lectura, ayuda a los usuarios a descubrir contenidos interesantes relacionados a su perfil. En este trabajo, se propone un modelo para una plataforma de recomendación de contenidos basado en la retroalimentación implícita que ayude a los usuarios a descubrir contenidos de su interés de forma automática y dinámica. Palabras ClaveACRIE. GIUG, libros electrónicos, retroalimentación implícita, retroalimentación explicita, Sistemas de recomendación.   Abstract A content recommendation system for intelligent electronic books can build collective knowledge to a set of social network users. Based on the analysis of the behavior, preferences and background reading, helps users discover interesting content related to their profile. In this paper, we propose a model for a content recommendation platform based on implicit feedback to help users to discover content on their interest, automatically and dynamically. Keywords ACRIE, eBooks, GIUG, implicit feedback, explicit feedback, recommendation systems. 

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