scholarly journals Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 42 ◽  
Author(s):  
Camila Sundermann ◽  
Marcos Domingues ◽  
Roberta Sinoara ◽  
Ricardo Marcacini ◽  
Solange Rezende 
Keyword(s):  
Systematic Review ◽  
Recommender Systems ◽  
Digital Libraries ◽  
Opinion Mining ◽  
Contextual Information ◽  
Context Aware ◽  
Extraction Techniques ◽  
Current Context ◽  
Rich Information ◽  
The Rich

Recommender systems help users by recommending items, such as products and services, that can be of interest to these users. Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user’s current context (e.g., location and time). Moreover, the advent of Web 2.0 and the growing popularity of social and e-commerce media sites have encouraged users to naturally write texts describing their assessment of items. There are increasing efforts to incorporate the rich information embedded in user’s reviews/texts into the recommender systems. Given the importance of this type of texts and their usage along with opinion mining and contextual information extraction techniques for recommender systems, we present a systematic review on the recommender systems that explore both contextual information and opinion mining. This systematic review followed a well-defined protocol. Its results were based on 17 papers, selected among 195 papers identified in four digital libraries. The results of this review give a general summary of the current research on this subject and point out some areas that may be improved in future primary works.

scholarly journals Paas EUD Tool for Developing Expert Context-Aware Mobile Applications

2021 ◽  
Vol 10 (2) ◽  
pp. 215-224
Author(s):  
Sahar Elshafei ◽  
◽  
Ehab Hassanein ◽  
Hanan Elazhary ◽  
◽  
...  
Keyword(s):  
Mobile Applications ◽  
Expert Knowledge ◽  
Contextual Information ◽  
Quality Data ◽  
Software Systems ◽  
Context Aware ◽  
Software Developers ◽  
Platform As A Service ◽  
Context Data ◽  
Current Context

Context-awareness enables systems to be tailored to the needs of users and their real circumstances at certain times. A noteworthy trend in software development is that an increasing number of software systems are being developed by individuals with expert knowledge in other sectors. Because most of the current context-aware development toolkits are intended for software developers, these types of systems cannot be easily developed by non-technical consumers. The development of tools for designing context-aware frameworks by consumers who are not programming experts but are specialists in the area of implementation would result in faster adoption of such services by businesses. This paper provides a cloud-based framework for people without programming experience to create context-aware mobile applications. The platform can provide a lightweight distribution of packaged applications that allows experts to send specified information to mobile users based on their context data without overlapping between the rules of the application. An energy-efficient mobile application was developed to acquire contextual information from the user device and to create quality data accordingly. The framework adopts Platform as a Service (PaaS) and containerization to facilitate development of context-aware mobile applications by experts in various domains rather than developing a tool for each domain in isolation, while considering multitenancy.

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

scholarly journals User models for multi-context-aware music recommendation

2020 ◽  
Author(s):  
Martin Pichl ◽  
Eva Zangerle
Keyword(s):  
Recommender Systems ◽  
User Model ◽  
Context Aware ◽  
Music Recommendation ◽  
Music Consumption ◽  
Current Context ◽  
Music Recommender Systems ◽  
Context Features ◽  
Content Information ◽  
Music Streaming

Abstract In the last decade, music consumption has changed dramatically as humans have increasingly started to use music streaming platforms. While such platforms provide access to millions of songs, the sheer volume of choices available renders it hard for users to find songs they like. Consequently, the task of finding music the user likes is often mitigated by music recommender systems, which aim to provide recommendations that match the user’s current context. Particularly in the field of music recommendation, adapting recommendations to the user’s current context is critical as, throughout the day, users listen to different music in numerous different contexts and situations. Therefore, we propose a multi-context-aware user model and track recommender system that jointly exploit information about the current situation and musical preferences of users. Our proposed system clusters users based on their situational context features and similarly, clusters music tracks based on their content features. By conducting a series of offline experiments, we show that by relying on Factorization Machines for the computation of recommendations, the proposed multi-context-aware user model successfully leverages interaction effects between user listening histories, situational, and track content information, substantially outperforming a set of baseline recommender systems.

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.

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.

A systematic review of scholar context-aware recommender systems

2015 ◽  
Vol 42 (3) ◽  
pp. 1743-1758 ◽  
Author(s):  
Zohreh Dehghani Champiri ◽  
Seyed Reza Shahamiri ◽  
Siti Salwah Binti Salim
Keyword(s):  
Systematic Review ◽  
Recommender Systems ◽  
Context Aware
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