Mobile recommender systems: Identifying the major concepts

@article{Pimenidis2018MobileRS,
  title={Mobile recommender systems: Identifying the major concepts},
  author={Elias Pimenidis and Nikolaos Polatidis and Haralambos Mouratidis},
  journal={Journal of Information Science},
  year={2018},
  volume={45},
  pages={387 - 397}
}
This article identifies the factors that have an impact on mobile recommender systems. Recommender systems have become a technology that has been widely used by various online applications in situations where there is an information overload problem. Numerous applications such as e-Commerce, video platforms and social networks provide personalised recommendations to their users and this has improved the user experience and vendor revenues. The development of recommender systems has been focused… 

Figures and Tables from this paper

A Framework for Mobile Personalized-Based Recommender System Using Social Tag Clustering Approach

This study explores the work in mobile recommendation systems and proposes a framework for developing a personalized-based recommendation system for mobile application, utilizing a social tag clustering approach, and indicates that the similarity measure affects the recommendation results.

Privacy Concerns and Remedies in Mobile Recommender Systems (MRSs)

This study intends to provide a comprehensive review of privacy concerns associated with data collection in MRSs and offers insights into how these privacy issues can be addressed.

PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile Applications

A hybrid Apps recommendation framework which is considering the version of the mobile Apps is proposed, which helps in resolving cold start problems of new users and integrates the principles of Probabilistic Matrix Factorization with Version Evolution Progress Model.

Relevancy or Diversity?: Recommendation Strategy Based on the Degree of Multi-Context Use of News Feed Users

Reading information in news feed apps has become a kind of popular content consumption in recent years. However, there are contradictory conclusions about the recommendation strategies. Although some

A collaborative filtering algorithm based on item labels and Hellinger distance for sparse data

The proposed prediction method can significantly improve the utilisation of neighbours and obviously increase the accuracy of prediction, and the experimental results confirm that the proposed algorithm can effectively alleviate the sparse data problem and improve the recommendation results.

Metamorphic Robustness Testing for Recommender Systems: A Case Study

This paper proposes a solution of applying metamorphic testing to validate the robustness of recommender systems, and shows that these methods can be regarded as a quality evaluation approach for recommendation algorithms or models.

CityCross: Transferring Attention-based Knowledge for Location-based Advertising Recommendation

This work proposes a novel location-based ad-vertising recommendation framework: CityCross, which is dedicated to learning the relevant knowledge of advertisement in a new domain by utilizing the attention-based semantic information, cross-city knowledge association, and the local neighbors' knowledge.

A Framework for Context-Aware Query Processing

  • Romani Farid Ibrahim
  • Computer Science
    2021 6th International Conference on Inventive Computation Technologies (ICICT)
  • 2021
Using natural language processing (NLP) and artificial intelligence techniques to handle database queries can make query results more useful and accurate, especially in context-aware applications,

Who Wants to Use an Augmented Reality Shopping Assistant Application?

An instantiation of a XARSAA artifact is developed, which is artificially evaluated through a controlled online user experiment with 315 participants, and results illustrate multiple demographics which influence customers attitude towards an augmented reality shopping assistant application in brick-and-mortar stores.

Exploring proximity-based recommendation criteria as a tool for information exchange and interactions between locals and tourists

An extended analysis on the opportunity to use people-to-people recommendation criteria based on proximity on the basis of profile similarity, geographical proximity, and random exploration is presented.

References

SHOWING 1-10 OF 53 REFERENCES

Mobile Recommender Systems

  • F. Ricci
  • Computer Science
    J. Inf. Technol. Tour.
  • 2010
The major issues and opportunities that the mobile scenario opens to the application of recommender systems especially in the area of travel and tourism are reviewed and some possible future developments and extension are presented.

The Anatomy of Mobile Location-Based Recommender Systems

  • N. Lathia
  • Computer Science
    Recommender Systems Handbook
  • 2015
This chapter reviews the main components of a mobile location-based recommender system: the data that can be used to learn about users and items, the algorithms that have been applied to recommending venues, and the techniques that researchers have used to evaluate the quality of these recommendations.

Recommender system application developments: A survey

A Survey of Context-Aware Mobile Recommendations

A focused survey of the recent development of context-aware mobile recommendations, including the privacy problem, the energy efficiency issues, and the design of user interfaces is provided.

Context-aware Recommender Systems using Data Mining Techniques

A novel recommender system to provide the advertisements of context-aware services that employs a classification rule to understand users’ needs type using a decision tree algorithm.

Recommender systems: from algorithms to user experience

It is argued that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and additional measures that have proven effective are suggested.

Recommender systems survey

A novel mobile recommender system for indoor shopping

Recommender Systems - An Introduction

An overview of approaches to developing state-of-the-art recommender systems, including current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches.

Towards a Context-Aware Photo Recommender System

The MMedia2U is presented, a prototype of a mobile photo recommender system that exploits the user’s context and the context when the photo was created as a means to improve the recommendation.
...