Location Aware Personalized News Recommender System Based on Twitter Popularity
02 May 2018-pp 650-658
TL;DR: This work proposes an approach to recommend the personalized news to the users based on their individual preferences and believes that the interest of the user, popularity of article and other attributes of news are implicitly fuzzy in nature and therefore this is exploited for generating the recommendation score for articles to be recommended.
Abstract: The mobile and handheld devices have become an indispensable part of life in this era of technological advancement. Further, the ubiquity of location acquisition technologies like global positioning system (GPS) has opened the new avenues for location aware applications for mobile devices. Reading online news is becoming increasingly popular way to gather information from news sources around the globe. Users can search and read the news of their preference wherever they want. The news preferences of individuals are influenced by several factors including the geographical contexts and the recent trends on social media. In this work we propose an approach to recommend the personalized news to the users based on their individual preferences. The model for user preferences are learned implicitly for individual users. Also, the popularity of trending articles floating around the twitter are exploited to provide news interesting recommendations to the user. We believe that the interest of the user, popularity of article and other attributes of news are implicitly fuzzy in nature and therefore we propose to exploit this for generating the recommendation score for articles to be recommended. The prototype is developed for testing and evaluation of proposed approach and the results of the evaluation are motivating.
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TL;DR: In this paper, a comprehensive review of context-aware recommender systems developed for online social networks is presented, which clearly defined the scope, the objective, the timeframe, the methods, and the tools to undertake this research.
Abstract: Context-aware recommender systems dedicated to online social networks experienced noticeable growth in the last few years. This has led to more research being done in this area stimulated by the omnipresence of smartphones and the latest web technologies. These systems are able to detect specific user needs and adapt recommendations to actual user context. In this research, we present a comprehensive review of context-aware recommender systems developed for social networks. For this purpose, we used a systematic literature review methodology which clearly defined the scope, the objective, the timeframe, the methods, and the tools to undertake this research. Our focus is to investigate approaches and techniques used in the development of context-aware recommender systems for social networks and identify the research gaps, challenges, and opportunities in this field. In order to have a clear vision of the research potential in the field, we considered research articles published between 2015 and 2020 and used a research portal giving access to major scientific research databases. Primary research articles selected are reviewed and the recommendation process is analyzed to identify the approach, the techniques, and the context elements employed in the development of the recommendation systems. The paper presents the detail of the review study, provides a synthesis of the results, proposes an evaluation based on measurable evaluation tools developed in this study, and advocates future research and development pathways in this interesting field.
13 citations
TL;DR: This work identifies and thoroughly investigates country profiles of music preferences on the fine-grained level of music tracks and proposes a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models.
Abstract: Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the "music mainstream" strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervised learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user's country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-a-vis state-of-the-art algorithms that do not exploit this type of context information.
6 citations
02 Feb 2021
TL;DR: In this article, a multi-layer generative model based on a variational autoencoder is proposed for music recommendation, where contextual features can influence recommendations through a gating mechanism.
Abstract: Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the "music mainstream" strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervized learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user's country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-a-vis state-of-the-art algorithms that do not exploit this type of context information.
5 citations
18 Sep 2020
TL;DR: This survey summarizes news features-based recommendation methods including location-based news recommendation methods, time- based news recommendation Methods, events-basedNews recommendation methods.
Abstract: In recent years, many traditional news websites developed corresponding recommendation systems to cater to readers’ interests and news recommendation systems are widely applied in traditional PCs and mobile devices. News recommendation system has become a critical research hotspot in the field of recommendation system. As News contains more text information, it is more helpful to improve the recommendation effect to obtain the content related to news features (location, time, events) from the news. This survey summarizes news features-based recommendation methods including location-based news recommendation methods, time-based news recommendation methods, events-based news recommendation methods. It helps researchers to know the application of news features in news recommendation methods. Also, this suvery summarizes the challenges faced by the news recommendation system and the future research direction.
3 citations
Posted Content•
TL;DR: This paper summarizes the research progress regarding news recommendation methods and summarizes the recommendation methods that are combined with other methods based on each category of methods and according to the time sequence of research results.
Abstract: Due to researchers'aim to study personalized recommendations for different business fields, the summary of recommendation methods in specific fields is of practical significance. News recommendation systems were the earliest research field regarding recommendation systems, and were also the earliest recommendation field to apply the collaborative filtering method. In addition, news is real-time and rich in content, which makes news recommendation methods more challenging than in other fields. Thus, this paper summarizes the research progress regarding news recommendation methods. From 2018 to 2020, developed news recommendation methods were mainly deep learning-based, attention-based, and knowledge graphs-based. As of 2020, there are many news recommendation methods that combine attention mechanisms and knowledge graphs. However, these methods were all developed based on basic methods (the collaborative filtering method, the content-based recommendation method, and a mixed recommendation method combining the two). In order to allow researchers to have a detailed understanding of the development process of news recommendation methods, the news recommendation methods surveyed in this paper, which cover nearly 10 years, are divided into three categories according to the abovementioned basic methods. Firstly, the paper introduces the basic ideas of each category of methods and then summarizes the recommendation methods that are combined with other methods based on each category of methods and according to the time sequence of research results. Finally, this paper also summarizes the challenges confronting news recommendation systems.
1 citations
Cites background from "Location Aware Personalized News Re..."
...Also, this method uses two content indexes (one from Twitter and one from RSS feeds)....
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...[51] Tiwari, S.; Pangtey, M.S.; Kumar, S. Location Aware Personalized News Recommender System Based on Twitter Popularity....
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...Twitter is a popular social platform where you can get user location information [51] and topic information from Twitter via its API (https://api....
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...Reference [55] uses the popularity of topics on Twitter to sort news....
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...Use the user's Twitter information in [52, 53] to model the user profile and then recommend news articles for the user....
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References
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TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Abstract: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
9,873 citations
TL;DR: This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants, and shows that semantic ratings obtained from the knowledge- based part of the system enhance the effectiveness of collaborative filtering.
Abstract: Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques To improve performance, these methods have sometimes been combined in hybrid recommenders This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering
3,883 citations
26 Apr 2010
TL;DR: This work model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks.
Abstract: Personalized web services strive to adapt their services (advertisements, news articles, etc.) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation.In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks.The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.
2,467 citations
TL;DR: An overview of the multifaceted notion of context is provided, several approaches for incorporating contextual information in recommendation process are discussed, and the usage of such approaches in several application areas where different types of contexts are exploited are illustrated.
Abstract: Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware recommender systems.
1,370 citations
07 Feb 2010
TL;DR: This research presents the content-based recommendation mechanism which uses learned user profiles with an existing collaborative filtering mechanism to generate personalized news recommendations in Google News and demonstrates that the hybrid method improves the quality of news recommendation and increases traffic to the site.
Abstract: Online news reading has become very popular as the web provides access to news articles from millions of sources around the world. A key challenge of news websites is to help users find the articles that are interesting to read. In this paper, we present our research on developing personalized news recommendation system in Google News. For users who are logged in and have explicitly enabled web history, the recommendation system builds profiles of users' news interests based on their past click behavior. To understand how users' news interests change over time, we first conducted a large-scale analysis of anonymized Google News users click logs. Based on the log analysis, we developed a Bayesian framework for predicting users' current news interests from the activities of that particular user and the news trends demonstrated in the activity of all users. We combine the content-based recommendation mechanism which uses learned user profiles with an existing collaborative filtering mechanism to generate personalized news recommendations. The hybrid recommender system was deployed in Google News. Experiments on the live traffic of Google News website demonstrated that the hybrid method improves the quality of news recommendation and increases traffic to the site.
737 citations