Modeling and broadening temporal user interest in personalized news recommendation

Li, Lei; Zheng, Li; Yang, Fan; Li, Tao

doi:10.1016/j.eswa.2013.11.020

Cited by 182 publications

(129 citation statements)

References 14 publications

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“…Change of interests [26], [6], is a typical example of concept drift. To this end, Zliobaite et al [27] develop an intelligent approach for sales prediction, which uses a mechanism for model switching, depending on the sales behavior of a product.…”

Section: Related Workmentioning

confidence: 99%

Using Time Clusters for Following Users’ Shifts in Rating Practices

Margaris

Vassilakis

2018

J. Complex Syst. Inform. Model. Q.

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Abstract. Users that enter ratings for items follow different rating practices, in the sense that, when rating items, some users are more lenient, while others are stricter. This aspect is taken into account by the most widely used similarity metric in user-user collaborative filtering, namely, the Pearson Correlation, which adjusts each individual user rating by the mean value of the ratings entered by the specific user, when computing similarities. However, a user's rating practices change over time, i.e. a user could start as strict and subsequently become lenient or vice versa. In that sense, the practice of using a single mean value for adjusting users' ratings is inadequate, since it fails to follow such shifts in users' rating practices, leading to decreased rating prediction accuracy. In this work, we address this issue by using the concept of dynamic averages introduced earlier and we extend earlier work by (1) introducing the concept of rating time clusters and (2) presenting a novel algorithm for calculating dynamic user averages and exploiting them in useruser collaborative, filtering implementations. The proposed algorithm incorporates the aforementioned concept and is able to follow more successfully shifts in users' rating practices. It has been evaluated using numerous datasets, and has been found to introduce significant gains in rating prediction accuracy, while outperforming the dynamic average computation approaches that are presented earlier.

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Section: Related Workmentioning

confidence: 99%

Using Time Clusters for Following Users’ Shifts in Rating Practices

Margaris

Vassilakis

2018

J. Complex Syst. Inform. Model. Q.

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“…If a node labeled MIS[j] is found, then the path from root to it in the tree can be obtained and denoted as PATH as shown in line 7. Then for each node denoted by Node in PATH, its weight is incrementally updated into MIC-tree by lines [8][9][10][11][12][13][14][15][16][17][18][19][20] According to the process of Algorithm 1, only the paths from root to those nodes which register the meta-interests of user in prepared concept hierarchy tree are copied to build the MIC-tree. Thus an MIC-tree is far smaller than the concept hierarchy tree.…”

Section: B Incrementally Updating Uimmentioning

confidence: 99%

“…The existing user interest models can be primarily divided into two classifications: static user model [2] and dynamic user model [6] [9]. The former can extract and represent user's basic information or behavior information by mining a static dataset, which is useful in personalized recommendation but cannot deal with the dynamicity of user interest over time.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al [6] analyzed the reading history of users and present a news recommender system LOGO which integrates the long-term and short-term reading preferences of users. LOGO can help determine the news recommendation list to individual users.…”

Section: Introductionmentioning

confidence: 99%

“…According to the implementation techniques are used to represent user interest, the user interest models can also be classified into four categories: traditional bag of words (BOW) based [10], concepts based [8,11], tag based [12] and topic based [6,9,13]. Additionally, semantic enrichment process [11] [8] are also used to enhance the scope of the words used to represent user interests, and to provide a prediction for new interests of the user.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

User Interest Model at Multiple Dimensions and Multiple Granularities

Chen¹,

Deng²

2016

Proceedings of the 2016 International Conference on Computer Engineering and Information Systems

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Abstract-Finding user's interest and pushing related resources to user would be the best solution to aim the problem of "Information Overload" and "Lost in Internet". This paper designs a user interest model with multiple dimensions and multiple granularities, named UIM. In UIM, the interest concepts are kept by a hierarchical concept tree at different granularities, the history of interest are maintained with titled time window at multiple time granularities, and moreover current interest are discriminated from history ones by gradually fading the weights of interests. The results of simulations show that UIM is efficient to be maintained, can quickly extract users important interests in different time windows, and the correctness of finding user interest is prior to analogous methods.

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Story‐focused reading in online news and its potential for user engagement

Lehmann

Castillo

Lalmas

et al. 2016

Asso for Info Science & Tech

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We study the news reading behavior of several hundred thousand users on 65 highly visited news sites. We focus on a specific phenomenon: users reading several articles related to a particular news development, which we call story-focused reading. Our goal is to understand the effect of story-focused reading on user engagement and how news sites can support this phenomenon. We found that most users focus on stories that interest them and that even casual news readers engage in story-focused reading. During story-focused reading, users spend more time reading and a larger number of news sites are involved. In addition, readers employ different strategies to find articles related to a story. We also analyze how news sites promote story-focused reading by looking at how they link their articles to related content published by them, or by other sources. The results show that providing links to related content leads to a higher engagement of the users, and that this is the case even for links to external sites. We also show that the performance of links can be affected by their type, their position, and how many of them are present within an article.

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Modeling and broadening temporal user interest in personalized news recommendation

Cited by 182 publications

References 14 publications

Using Time Clusters for Following Users’ Shifts in Rating Practices

Using Time Clusters for Following Users’ Shifts in Rating Practices

User Interest Model at Multiple Dimensions and Multiple Granularities

Story‐focused reading in online news and its potential for user engagement

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