This paper reviews the research trends that link the advanced technical aspects of recommendation systems that are used in various service areas and the business aspects of these services. First, for a reliable analysis of recommendation models for recommendation systems, data mining technology, and related research by application service, more than 135 top-ranking articles and top-tier conferences published in Google Scholar between 2010 and 2021 were collected and reviewed. Based on this, studies on recommendation system models and the technology used in recommendation systems were systematized, and research trends by year were analyzed. In addition, the application service fields where recommendation systems were used were classified, and research on the recommendation system model and recommendation technique used in each field was analyzed. Furthermore, vast amounts of application service-related data used by recommendation systems were collected from 2010 to 2021 without taking the journal ranking into consideration and reviewed along with various recommendation system studies, as well as applied service field industry data. As a result of this study, it was found that the flow and quantitative growth of various detailed studies of recommendation systems interact with the business growth of the actual applied service field. While providing a comprehensive summary of recommendation systems, this study provides insight to many researchers interested in recommendation systems through the analysis of its various technologies and trends in the service field to which recommendation systems are applied.

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A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields

Recommender systems have greatly evolved in recent years and have become an integral part of the Web. From e-commerce sites to mobile apps, our daily routine revolves around a series of “small” decisions that are influenced by such recommendations. In a similar manner, online social networks recommend only a subset of the massive amount of content published by a user’s friends. However, the prevalent approach for the content selection process in such systems is driven by the amount of interaction between the user and the friend who published the content. As a result, content of interest is often lost due to weak social ties. In this paper, we present a fine-grained recommender system for social ecosystems, designed to recommend media content (e.g., music videos, online clips) published by the user’s friends. The system design was driven by the findings of our qualitative user study that explored the value and requirements of a recommendation component within a social network. The core idea behind the proposed approach was to leverage the abundance of preexisting information in each user’s account for creating interest profiles, to calculate similarity scores at a fine-grained level for each friend. The intuition behind the proposed method was to find consistent ways to obtain information representations that can identify overlapping interests in very specific sub-categories (e.g., two users’ music preferences may only coincide on hard rock). While the system is intended as a component of the social networking service, we developed a proof-of-concept implementation for Facebook and explored the effectiveness of our underlying mechanisms for content analysis. Our experimental evaluation demonstrates the effectiveness of our approach, as the recommended content of interest was both overlooked by the existing Facebook engine and not contained in the users’ Facebook News Feed. We also conducted a user study for exploring the usability aspects of the prototype and found that it offers functionality that could significantly improve user experience in popular services.

A fine-grained social network recommender system

User experience design and subsequent usability evaluation can benefit from knowledge about user interaction, types, deployment settings and situations. Most of the time, the user type and generic requirements are given or can be obtained and used to model interaction during the design phase. The deployment settings and situations can be collected through the needfinding phase, either via user feedback or via the automatic analysis of existing data. Personas may be defined using the aforementioned information through user research analysis or data analysis. This work utilizes an approach to activate an accurate persona definition early in the design cycle, using topic detection to semantically enrich the data that are used to derive the persona details. This work uses Twitter data from a music event to extract information that can be used to assist persona creation. A user study in persona construction compares the topic modelling metadata to a traditional user collected data analysis for persona construction. The results show that the topic information-driven constructed personas are perceived as having better clarity, completeness and credibility. Additionally, the human users feel more attracted and similar to such personas. This work may be used to model personas and recommend suitable ones to designers of other products, such as advertisers, game designers and moviegoers.

https://www.mdpi.com/2504-2289/4/3/21/pdf

Data-Assisted Persona Construction Using Social Media Data

Traditional recommendation approaches for the mobile Apps basically depend on the Apps related features. Now a days many users are in quench of Apps recommendation based on the version description. Earlier mobile Apps recommendation system do not handle the cold start problem and also lacks in time for recommending the related and latest version of Apps. To overcome this issues, a hybrid Apps recommendation framework which is considering the version of the mobile Apps is proposed. This novel framework named “Probabilistic Evolution based Version Recommendation Model (PEVRM)” integrates the principles of Probabilistic Matrix Factorization (PMF) with Version Evolution Progress Model (VEPM). With the help this novel recommendation algorithm, the mobile users easily identify the specific Apps for particular task based on its version progression. At same time, this framework helps in resolving cold start problems of new users. Evaluations of this framework utilize a benchmark dataset, i.e., Apple’s iTunes App Store3, for revealing its promising performance.

/pdf/pevrm-probabilistic-evolution-based-version-recommendation-480ag0f4w8.pdf

PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile Applications

Social media has evolved itself as a significant tool used by people for information spread during emergencies like natural or man-made disasters. Real-time analysis of this huge collected data can play a vital role in crisis estimation, response and assistance exercises. We propose a novel prototype system that analyzes the emergency-related tweets to classify them as need or available tweets. The presented system also takes care of non-English tweets as there is no boundary of language for social media users. Several classifiers along with different learning methodologies are used to show their usefulness for an efficient solution. Here, a new supervised learning technique based on word embedding is incorporated in the novel hybrid model that comprises of LSTM and CNN. The system will further give a ranked list of tweets, along with a relevance score for each tweet with respect to the topic. Finally for each of the identified need tweets, its corresponding availability tweets are mapped. For the mapping task, a novel two-word sliding window approach is proposed to generate the combine embedding of two adjacent words. The experimental results show significant improvement in the performance. We evaluate our proposed system with FIRE-2016 and CrisisLex datasets to illustrate its effectiveness during mobilization of needful resources.

A deep learning-based social media text analysis framework for disaster resource management

Content selection in social networks is driven by numerous extraneous factors that can result in the loss of content of interest. In this paper we present Reveal, a fine-grained recommender system for social networks, designed to recommend media content posted by the user’s friends. The intuition is to leverage the abundance of pre-existing information and identify overlapping user interests in specific sub-categories. While our system is intended as a component of the social network, we develop a proof-of-concept implementation for Facebook and experimentally evaluate the effectiveness of our approach.

Markos Aivazoglou

Papers

A fine-grained social network recommender system

Reveal: Fine-grained Recommendations in Online Social Networks