A Hybrid Filtering Approach for an Improved Context-aware Recommender System

2019 ◽  
Vol 13 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Mugdha Sharma ◽  
Laxmi Ahuja ◽  
Vinay Kumar
Keyword(s):  
Recommender Systems ◽  
Performance Improvement ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Context Aware ◽  
Post Evaluation ◽  
Contextual Modeling ◽  
Filter Approach ◽  
Single Approach ◽  
Post Filter

Background: The domain of context-aware recommender approaches has made a substantial advancement over the last decade, but many applications still do not include contextual information while providing recommendations. Contextual information is crucial for various application areas and should not be ignored. Objective: There are generally three algorithms which can be used to include context and those are - pre-filter approach, post-filter approach and contextual modeling. Each of the algorithms has their own drawbacks if any single approach is chosen. The goal of this work is to identify and propose a new hybrid approach which can include contextual information to improve the current movie recommender systems. Method: Post evaluation of various patents related to recommender systems, the proposed approach modifies the post filter approach to rectify its shortcomings and combines it with the pre-filter approach based on the importance of contextual attribute provided by the user. Results: The performance of the proposed system is measured in terms of precision of the system and ranking of the recommended movies to the user. The results of experimental setup also demonstrate that the proposed system improves the precision and ranking of the recommendations provided to the user. Conclusion: With the help of this hybrid approach, the proposed system eliminates the problem of sparsity which is present in the pre-filter algorithm, and has performance improvement over the traditional post-filter approach. The proposed system will be vital for movie ticketing brands for the promotional purposes and various online content providers to recommend the accurate movies to their users.

A Hybrid Context Aware Recommender System with Combined Pre and Post-Filter Approach

2019 ◽  
Vol 10 (4) ◽  
pp. 1-14 ◽  
Author(s):  
Mugdha Sharma ◽  
Laxmi Ahuja ◽  
Vinay Kumar
Keyword(s):  
Performance Improvement ◽  
Recommender System ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Experimental Setup ◽  
Context Aware ◽  
Contextual Modeling ◽  
Filter Approach ◽  
Post Filter

The domain of context aware recommender approaches has made substantial advancement over the last decade, but many applications still do not include contextual information while providing recommendations. Contextual information is crucial for various application areas and should not be ignored. There are generally three algorithms which can be used to include context and those are: pre-filter approach, post-filter approach, and contextual modeling. Each of the algorithms has their own drawbacks. The proposed approach modifies the post filter approach to rectify its shortcomings and combines it with the pre-filter approach based on the importance of contextual attribute provided by the user. The results of experimental setup also demonstrate that the proposed system improves the precision and ranking of the recommendations provided to user. With the help of this hybrid approach, the proposed system eliminates the problem of sparsity which is present in the pre-filter algorithm, and has performance improvement over the traditional post-filter approach.

scholarly journals Context-Aware and Sequential Pattern Mining based recommendations for Research Papers: A Hybrid Approach

2020 ◽  
Author(s):  
Hafiz Amaad ◽  
Naveed Jhamat ◽  
Kashif Riaz ◽  
Zeeshan Arshad
Keyword(s):  
Context Awareness ◽  
Pattern Mining ◽  
Information Overload ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Absolute Error ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Context Aware ◽  
Research Papers

The availability of huge volumes of online research papers over scholarly communities has been increasing rapidly with the evolution of the Internet. Meanwhile, several researchers confront troubles while retrieving suitable and relevant research papers according to their research necessities due to information overload. Besides, the research necessities vary from researcher to researcher according to their contextual state and the online behavior in sequential access. Conventional recommendation approaches for instance content-based filtering (CBF) and collaborative filtering (CF) utilize content features and rankings correspondingly, in order to produce recommendations for the researchers. In spite of this, it is inevitable to incorporate scholar’s contextual information and sequential access behavior into the recommendation procedure to generate accurate and personalized recommendations for research papers. Conventional recommender systems do not incorporate such information in the recommended procedure to compute similarities of scholars and provide recommendations; thus, they are more liable to produce an irrelevant list of recommendations in a scholarly environment. Moreover, conventional recommendation approaches generate inaccurate recommendations in presence of a high level of sparsity in the rankings. In this article, we introduce a novel method for research paper recommendations that incorporates the benefits of collective filtering (CF), context-awareness, and sequential pattern mining (SPM) to propose research papers to scholars in a hybrid manner. Context-awareness in our methodology involves the scholar's contextual state, such as skill level and research goals; SPM is used to mine weblogs and reveal sequential access actions of scholars, and CF is used to measure predictions based on correlations between scholars and generate context-aware and sequential trend mining based recommendations for the targeted scholars. Experimental evaluations of our approach indicate the excellence of our approach over other baseline approaches in terms of precision, recall, F1, and mean absolute error (MAE).

Transforming Geo-Referenced Data in Contextual Information for Context-Aware Recommender Systems

2018 ◽  
Author(s):  
Igor Andre Santana ◽  
Abner Suniga ◽  
Juliano Donini ◽  
Camila Vaccari Sundermann ◽  
Solange Oliveira Rezende ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Contextual Information ◽  
Context Aware

Context-Aware Pervasive Services for Smart Cities

2011 ◽  
pp. 1-16 ◽  
Author(s):  
René Meier ◽  
Deirdre Lee
Keyword(s):  
Intelligent Transportation Systems ◽  
Data Model ◽  
Programming Model ◽  
Smart Cities ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Transportation Systems ◽  
Context Aware ◽  
Transportation Services ◽  
Context Modelling

Smart environments support the activities of individuals by enabling context-aware access to pervasive information and services. This article presents the iTransIT framework for building such context-aware pervasive services in Smart Cities. The iTransIT framework provides an architecture for conceptually integrating the independent systems underlying Smart Cities and a data model for capturing the contextual information generated by these systems. The data model is based on a hybrid approach to context-modelling that incorporates the management and communication benefits of traditional object-based context modelling with the semantic and inference advantages of ontology-based context modelling. The iTransIT framework furthermore supports a programming model designed to provide a standardised way to access and correlate contextual information from systems and ultimately, to build context-aware pervasive services for Smart Cities. The framework has been assessed based on a prototypical realisation of an architecture for integrating diverse intelligent transportation systems in Dublin and by building context-aware pervasive transportation services for urban journey planning and for visualising traffic congestion.

Using Contextual Information from Topic Hierarchies to Improve Context-Aware Recommender Systems

2014 ◽  
Author(s):  
Marcos Aurelio Domingues ◽  
Marcelo Garcia Manzato ◽  
Ricardo Marcondes Marcacini ◽  
Camila Vaccari Sundermann ◽  
Solange Oliveira Rezende
Keyword(s):  
Recommender Systems ◽  
Contextual Information ◽  
Context Aware

Privileged contextual information for context-aware recommender systems

2016 ◽  
Vol 57 ◽  
pp. 139-158 ◽  
Author(s):  
Camila Vaccari Sundermann ◽  
Marcos Aurélio Domingues ◽  
Merley da Silva Conrado ◽  
Solange Oliveira Rezende
Keyword(s):  
Recommender Systems ◽  
Contextual Information ◽  
Context Aware

scholarly journals A Soft-Rough Set Based Approach for Handling Contextual Sparsity in Context-Aware Video Recommender Systems

Mathematics ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 740 ◽  
Author(s):  
Syed Manzar Abbas ◽  
Khubaib Amjad Alam ◽  
Shahaboddin Shamshirband
Keyword(s):  
Recommender Systems ◽  
Rough Sets ◽  
Selection Process ◽  
Contextual Information ◽  
Context Aware ◽  
User Interactions ◽  
Information Selection ◽  
Proposed Model ◽  
Research Challenge ◽  
Selection Of

Context-aware video recommender systems (CAVRS) seek to improve recommendation performance by incorporating contextual features along with the conventional user-item ratings used by video recommender systems. In addition, the selection of influential and relevant contexts has a significant effect on the performance of CAVRS. However, it is not guaranteed that, under the same contextual scenario, all the items are evaluated by users for providing dense contextual ratings. This problem cause contextual sparsity in CAVRS because the influence of each contextual factor in traditional CAVRS assumes the weights of contexts homogeneously for each of the recommendations. Hence, the selection of influencing contexts with minimal conflicts is identified as a potential research challenge. This study aims at resolving the contextual sparsity problem to leverage user interactions at varying contexts with an item in CAVRS. This problem may be investigated by considering a formal approximation of contextual attributes. For the purpose of improving the accuracy of recommendation process, we have proposed a novel contextual information selection process using Soft-Rough Sets. The proposed model will select a minimal set of influencing contexts using a weights assign process by Soft-Rough sets. Moreover, the proposed algorithm has been extensively evaluated using “LDOS-CoMoDa” dataset, and the outcome signifies the accuracy of our approach in handling contextual sparsity by exploiting relevant contextual factors. The proposed model outperforms existing solutions by identifying relevant contexts efficiently based on certainty, strength, and relevancy for effective recommendations.

A State-of-the-Art Survey on Context-Aware Recommender Systems and Applications

2021 ◽  
Vol 12 (3) ◽  
pp. 1-20
Author(s):  
Quang-Hung Le ◽  
Son-Lam Vu ◽  
Thi-Kim-Phuong Nguyen ◽  
Thi-Xinh Le
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Contextual Information ◽  
Information Filtering ◽  
Weather Conditions ◽  
Research Literature ◽  
Future Research ◽  
Context Aware ◽  
Good Tool ◽  
Advantages And Disadvantages

In the digital transformation era, increasingly more individuals and organizations use or create services in digital spaces. Many business transactions have been moving from the offline to online mode. For example, sellers intend to introduce their products on e-commerce platforms rather than display them on store shelves as in traditional business. Although this new format business has advantages, such as more space for product displays, more efficient searches for a specific item, and providing a good tool for both buyers and sellers to manage their products, it is also accompanied by the obviously important problem that users are confused when choosing an appropriate item due to a large amount of information. For this reason, the need for a recommendation system appears. Informally, a recommender system is similar to an information filtering system that helps identify a set of items that best satisfy users' demands based on their preference profiles. The integration of contextual information (e.g., location, weather conditions, and user's mood) into recommender systems to improve their performance has recently received considerable attention in the research literature. However, incorporating such contextual information into recommendation models is a challenging task because of the increase in both the dimensionality and sparsity of the model. Different approaches with their own advantages and disadvantages have been proposed. This paper provides a comprehensive survey on context-aware recommender systems in recent years. In particular, the authors pay more attention to journal and conference proceedings papers published from 2016 to 2020. In addition, this paper also presents open issues for context-aware recommender systems and discuss promising directions for future research.

Review Aware Recommender System

2018 ◽  
Vol 10 (2) ◽  
pp. 28-50
Author(s):  
Fatima Zahra Lahlou ◽  
Houda Benbrahim ◽  
Ismail Kassou
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
State Of The Art ◽  
Contextual Information ◽  
Empirical Evaluation ◽  
Feature Engineering ◽  
Context Aware ◽  
Recommendation Accuracy

Context aware recommender systems (CARS) are recommender systems (RS) that provide recommendations according to user contexts. The first challenge for building such a system is to get the contextual information. Some works tried to get this information from reviews provided by users in addition to their ratings. However, all of these works perform important feature engineering in order to infer the context. In this article, the authors present a new CARS architecture that allows to automatically use contextual information from reviews without requiring any feature engineering. Moreover, they develop a new CARS algorithm that is tailored to textual contexts, that they call Textual Context Aware Factorization Machines (TCAFM). An empirical evaluation shows that the proposed architecture allows to significantly improve recommendation accuracy using state of the art RS and CARS algorithms, whereas TCAFM leads to additional improvements.

scholarly journals Privacy-preserving recommendations in context-aware mobile environments

2017 ◽  
Vol 25 (1) ◽  
pp. 62-79 ◽  
Author(s):  
Nikolaos Polatidis ◽  
Christos K. Georgiadis ◽  
Elias Pimenidis ◽  
Emmanouil Stiakakis
Keyword(s):  
Recommender Systems ◽  
Privacy Protection ◽  
Personal Information ◽  
Contextual Information ◽  
Context Aware ◽  
User Privacy ◽  
Data Set ◽  
Content Type ◽  
Privacy Concerns ◽  
Usability Tests

Purpose This paper aims to address privacy concerns that arise from the use of mobile recommender systems when processing contextual information relating to the user. Mobile recommender systems aim to solve the information overload problem by recommending products or services to users of Web services on mobile devices, such as smartphones or tablets, at any given point in time and in any possible location. They use recommendation methods, such as collaborative filtering or content-based filtering and use a considerable amount of contextual information to provide relevant recommendations. However, because of privacy concerns, users are not willing to provide the required personal information that would allow their views to be recorded and make these systems usable. Design/methodology/approach This work is focused on user privacy by providing a method for context privacy-preservation and privacy protection at user interface level. Thus, a set of algorithms that are part of the method has been designed with privacy protection in mind, which is done by using realistic dummy parameter creation. To demonstrate the applicability of the method, a relevant context-aware data set has been used to run performance and usability tests. Findings The proposed method has been experimentally evaluated using performance and usability evaluation tests and is shown that with a small decrease in terms of performance, user privacy can be protected. Originality/value This is a novel research paper that proposed a method for protecting the privacy of mobile recommender systems users when context parameters are used.

Sign in / Sign up

Export Citation Format

Share Document