Clustering-based diversity improvement in top-N recommendation

Aytekin, Tevfik; Karakaya, Mahmut

doi:10.1007/s10844-013-0252-9

Cited by 58 publications

(11 citation statements)

References 26 publications

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“…Diversity is generally applied to a set of items to determine how different the items are, and is defined in the literature as the average pairwise dissimilarity between recommended items (Bradley and Smyth, 2001). Diversity in recommendations is divided into two types: individual and aggregate diversity (Aytekin and Karakaya, 2014;Wu et al, 2014). Individual diversity is evaluated by computing the diversity of the recommendation list for a user, while aggregate diversity is calculated by aggregating the diversity of recommendations across all users.…”

Section: Recommendation Diversitymentioning

confidence: 99%

Integrating collaborative topic modeling and diversity for movie recommendations during news browsing

Liu¹,

Chou²,

Jian³

2019

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Purpose Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users’ satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles. Design/methodology/approach Association rule mining is applied to users’ news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website. Findings The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users’ online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance. Originality/value Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user’s online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.

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Section: Recommendation Diversitymentioning

confidence: 99%

Integrating collaborative topic modeling and diversity for movie recommendations during news browsing

Liu¹,

Chou²,

Jian³

2019

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“…ClusDiv [14] uses clustering to group items in the catalogue from the explicit ratings rather than on item descriptions (although this is not a necessary prerequisite), and recommends items from different clusters. Compared to re-ranking methods such as [9], using item clusters resulted quicker and achieved similar diversification results.…”

Section: Related Workmentioning

confidence: 99%

A User-Centric Diversity by Design Recommender System for the Movie Application Domain

Zanitti

Kosta

Sørensen

2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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Recommender systems (RS) have seen widespread adoption across the Internet. However, by emphasizing personalization through the optimization of accuracy-focused metrics, over-personalization may emerge, with negative effects on the user experience. A countermeasure to the problem is to diversify recommendations. In this paper, we present a solution that addresses the problem in the context of a movie application domain. The solution enhances diversity on four related dimensions, namely global coverage, local coverage, novelty, and redundancy. The proposed solution is designed to diversify users profiles, modeled on categorical preferences, within the same group in the recommendation filtering. We evaluate our approach on the Movielens dataset and show that our algorithm yields better results compared to random selection distant neighbors and performs comparably to one of the current state of the art solutions.

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“…Furthermore, a number of recent studies find that beyond accuracy there are other quality factors, which are also important to users. Some researchers focus on diversity and novelty (Adomavicius and Kwon 2012;Aytekin and Karakaya 2014). Some try to recommend long tail items to satisfy users (Park and Tuzhilin 2008).…”

Section: Evaluation Metricsmentioning

confidence: 99%

A hybrid approach of topic model and matrix factorization based on two-step recommendation framework

et al. 2014

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Recommender systems become increasingly significant in solving the information explosion problem. Two typical kinds of techniques treat the recommendation problem as either a rating prediction or a ranking prediction one. In contrast, we propose a two-step framework that considers recommendation as a simulation of users' behaviors to generate ratings. The first step is to predict the probability that a user rates an item, and the second step is to predict rating values. After that, the predicted results from both steps are combined to compute the expectations of users' ratings on items, which are used to generate recommendations. Based on this framework, we propose a hybrid approach which uses topic model in the first step and matrix factorization in the second to solve the recommendation problem. Experiments with MovieLens and EachMovie datasets demonstrate the effectiveness of the proposed framework and the recommendation approach.

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Clustering-based diversity improvement in top-N recommendation

Cited by 58 publications

References 26 publications

Integrating collaborative topic modeling and diversity for movie recommendations during news browsing

Integrating collaborative topic modeling and diversity for movie recommendations during news browsing

A User-Centric Diversity by Design Recommender System for the Movie Application Domain

A hybrid approach of topic model and matrix factorization based on two-step recommendation framework

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