Time-ordered collaborative filtering for news recommendation

Xiao, Yingyuan; Ai, Pengqiang; Hsu, Ching‐Hsien; Wang, Hongya; Xu, Jing

doi:10.1109/cc.2015.7385528

Cited by 51 publications

(23 citation statements)

References 19 publications

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“…As shown in Figure 1, we can learn that when ̸ = 0, MAE decreases with for any fixed K; i.e., the recommendation precision increases with for any fixed . This result is not surprising because represents the weight of ensuring the recommended precision in (8), and a larger means a higher recommendation precision. Furthermore, we can find from Figure 1 that MAE remains relatively stable when is greater than 0.8 which means that there is much less benefit to improve the prediction precision of recommendation by continually increase when it is larger the 0.8.…”

Section: Methodsmentioning

confidence: 81%

“…The model-based CF approaches leverage training datasets to train a predefined model [3][4][5][6][7][8]. The memorybased CF approaches are most relevant to the method proposed in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…In (8), denotes u's average rating; )| reflects the influence onf rom the users who have the most similar interested trends with u, which ensures the recommendation precision;…”

Section: The Proposed Collaborative Filteringmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing Collaborative Filtering by User-User Covariance Matrix

Xiao

Shi

Zheng

et al. 2018

Mathematical Problems in Engineering

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The collaborative filtering (CF) approach is one of the most successful personalized recommendation methods so far, which is employed by the majority of personalized recommender systems to predict users’ preferences or interests. The basic idea of CF is that if users had the same interests in the past they will also have similar tastes in the future. In general, the traditional CF may suffer the following problems: (1) The recommendation quality of CF based system is greatly affected by the sparsity of data. (2) The traditional CF is relatively difficult to adapt the situation that users’ preferences always change over time. (3) CF based approaches are used to recommend similar items to a user ignoring the user’s demand for variety. In this paper, to solve the above problems we build a new user-user covariance matrix to replace the traditional CF’s user-user similarity matrix. Compared with the user-user similarity matrix, the user-user covariance matrix introduces the user-user covariance to finely describe the changing trends of users’ interests. Furthermore, we propose an enhancing collaborative filtering method based on the user-user covariance matrix. The experimental results show that the proposed method can significantly improve the diversity of recommendation results and ensure the good recommendation precision.

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Section: Methodsmentioning

confidence: 81%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Collaborative Filtering by User-User Covariance Matrix

Xiao

Shi

Zheng

et al. 2018

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…More recently, Xiao et al . 50 proposes a time-ordered collaborative filtering recommendation algorithm (TOCF), which takes the time sequence characteristic of user behaviors into account. Moreover, a new method to compute the similarity among different users, named time-dependent similarity, is proposed.…”

Section: Collaborative Filtering Recommendation Systemsmentioning

confidence: 99%

A Hybrid Approach for Personalized News Recommendation in a Mobility Scenario Using Long-Short User Interest

Viana

Soares

2017

Int. J. Artif. Intell. Tools

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Access to information has been made easier in different domains that range from multimedia content, books, music, news, etc. To deal with the huge amount of alternatives, recommendation systems have been often used as a solution to filter the options and provide suggestions of items that might be of interest to an user. The news domain introduces additional challenges due not only to the large amount of new items produced daily but also due to their ephemeral timelife. In this paper, a news recommendation system which combines content-based and georeferenced techniques in a mobility scenario, is proposed. Taking into account the volatility of the information, short-term and long-term user profiles are considered and implicitly built. Besides tracking users’ clicks, the system infers different levels of interest an article has by tracking and weighting each action in the system and in social networks. Impact of the different fields that make up a news is also taken into account by following the inverted pyramid model that assumes different levels of importance to each paragraph of the article. The solution was tested with a population of volunteers and results indicate that the quality of the recommendation approach is acknowledged by the users.

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“…These updated similarities, transition characteristics and dynamic evolution patterns of users' preferences are considered. Xiao et al [12] propose a time-ordered collaborative filtering recommendation algorithm, which takes the time sequence characteristic of user behaviors into account. The above methods consider either time factor or similarity, a user interest recommendation based on collaborative filtering is proposed.…”

Section: Related Workmentioning

confidence: 99%

A User Interest Recommendation Based on Collaborative Filtering

Wu¹,

Wang²,

Liu³

et al. 2016

Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

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Abstract-The traditional collaborative filtering algorithm cannot response user interest with time and is lack of time effectiveness. These problems lead to poor recommendation quality. On the basis of the neighbor-based collaborative filtering, a fused method of improved similarity and user interest is proposed. To begin with, we compute similarity from global perspectives obtained with Jaccard similarity, local perspectives obtained with Bhattacharyya Coefficient. Furthermore, we adopt the forgetting curve to represent the user interest preference, adding the interest weight to the new similarity method to update user interest. Finally, we make recommendation prediction by calculating similarity using the method. Experimental results on the Movielens datasets demonstrate that our approach has advantages over state-of-the-art methods in terms of both the discovery of user interest preference and providing highly accuracy recommendations.

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Time-ordered collaborative filtering for news recommendation

Cited by 51 publications

References 19 publications

Enhancing Collaborative Filtering by User-User Covariance Matrix

Enhancing Collaborative Filtering by User-User Covariance Matrix

A Hybrid Approach for Personalized News Recommendation in a Mobility Scenario Using Long-Short User Interest

A User Interest Recommendation Based on Collaborative Filtering

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