Personalized News Recommendation: A Review and an Experimental Investigation

Li, Lei; Wang, Dingding; Zhu, Shengqi; Li, Tao

doi:10.1007/s11390-011-0175-2

Cited by 66 publications

(46 citation statements)

References 30 publications

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“…The news recommendation methods can be classified into three approaches, the content-based approach, the collaborative filtering approach, and hybrid-based approach [5] [7].…”

Section: Related Workmentioning

confidence: 99%

“…There have been several methods proposed for recommending news articles. They can be classified into two categories: a content-based approach and a collaborative filtering approach [5] [7]. The content-based approach uses a user profile built from the keywords of news articles that s/he clicked, and recommends the news articles whose keywords are similar to that of the user profile.…”

Section: Introductionmentioning

confidence: 99%

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A method for recommending the latest news articles via MinHash and LSH

Hwang

Park

Kim

2015

Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication

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Since most users are more interested in the latest news articles that are recently updated, it is important to recommend those news articles to appropriate users. However, existing methods cannot recommend the latest news articles in a short time. This paper proposes a novel recommendation method focusing on the latest news articles. It spends much shorter execution time than the existing methods thanks to employing two approximation methods, MinHash and locality sensitive hashing. For evaluation, we conducted extensive experiments using a real-world dataset. The experimental results show that our method provides better accuracy and performs much faster than the existing methods.

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“…The news recommendation methods can be classified into three approaches, the content-based approach, the collaborative filtering approach, and hybrid-based approach [5] [7].…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A method for recommending the latest news articles via MinHash and LSH

Hwang

Park

Kim

2015

Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication

View full text Add to dashboard Cite

show abstract

“…These methods aim to estimate the "relevance" or "utility" of different news articles for the users and use this information for generating the recommendation set. A brief mention of how these techniques are used for news recommendation can be found in [8], [10], [11]. [8], [10] model profiles of news articles based on the news content, users' access patterns and named entities.…”

Section: Related Workmentioning

confidence: 99%

“…A brief mention of how these techniques are used for news recommendation can be found in [8], [10], [11]. [8], [10] model profiles of news articles based on the news content, users' access patterns and named entities. User profiles are modeled based on (a) the topics and named entities that the users are interested in, and (b) user-similarity values as computed using collaborative filtering techniques.…”

Section: Related Workmentioning

confidence: 99%

Diversification in news recommendation for privacy concerned users

Desarkar

Shinde

2014

2014 International Conference on Data Science and Advanced Analytics (DSAA)

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With more and more news sources publishing news articles online, news recommendation systems are becoming increasingly popular in recent days. However, if a news recommender shows to the user many news articles that cover same story or talk about same persons or events, then the recommendations might become monotonous for the user. A possible way to handle this scenario is to diversify the recommended news articles so that the articles in the recommendation list are not too similar to each other. Existing research works in literature discuss how to use user-specific relevance of news articles and diversification to produce the final recommendations. To be able to compute user-specific relevance of articles, several recommender systems suggest that users have a login, or they set cookies to track page visits of the users. Often users are unsure whether the information collected via these means will be used for the sole purpose of news recommendation. Privacy of the identity and page access patterns is a concern for many users. In this work, we consider the problem where diverse recommendations are to be given for users, but no past data about users is collected or stored by the system. We use only article popularity to estimate the relevance. We identify few aspects of news articles on which diversification can be performed. These aspects are incorporated in an optimization framework. An approximation algorithm is used to generate the recommendations. Experimental analysis shows that in scenarios where no individual user data is stored or accessible by the recommender system, diversification results in higher click rates and increase in the number of satisfied users. Performances of the multiple diversification aspects and approaches are also analyzed.

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“…and additional items can be then inferred from this profile. CF algorithms are widely applied in areas in which the product contents are non-textual, such as music recommendation [3], news recommendation [4], movie recommendation [5], and product recommendation [6]. …”

Section: Introductionmentioning

confidence: 99%

A Personalized Electronic Movie Recommendation System Based on Support Vector Machine and Improved Particle Swarm Optimization

Wang¹,

Luo²,

Qian³

et al. 2016

PLoS ONE

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With the rapid development of ICT and Web technologies, a large an amount of information is becoming available and this is producing, in some instances, a condition of information overload. Under these conditions, it is difficult for a person to locate and access useful information for making decisions. To address this problem, there are information filtering systems, such as the personalized recommendation system (PRS) considered in this paper, that assist a person in identifying possible products or services of interest based on his/her preferences. Among available approaches, collaborative Filtering (CF) is one of the most widely used recommendation techniques. However, CF has some limitations, e.g., the relatively simple similarity calculation, cold start problem, etc. In this context, this paper presents a new regression model based on the support vector machine (SVM) classification and an improved PSO (IPSO) for the development of an electronic movie PRS. In its implementation, a SVM classification model is first established to obtain a preliminary movie recommendation list based on which a SVM regression model is applied to predict movies’ ratings. The proposed PRS not only considers the movie’s content information but also integrates the users’ demographic and behavioral information to better capture the users’ interests and preferences. The efficiency of the proposed method is verified by a series of experiments based on the MovieLens benchmark data set.

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Personalized News Recommendation: A Review and an Experimental Investigation

Cited by 66 publications

References 30 publications

A method for recommending the latest news articles via MinHash and LSH

A method for recommending the latest news articles via MinHash and LSH

Diversification in news recommendation for privacy concerned users

A Personalized Electronic Movie Recommendation System Based on Support Vector Machine and Improved Particle Swarm Optimization

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