Amazon.com recommendations: item-to-item collaborative filtering

Linden, Greg; Smith, Brent; York, Jeremy

doi:10.1109/mic.2003.1167344

Cited by 4,630 publications

(2,520 citation statements)

References 6 publications

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“…We have chosen this algorithm because it is widely used to recommend items when the modeling of user preferences is not a valid option (as in most of real scenarios [17][18][19] and others [20][21][22][23]). This algorithm requires users to rate an initial set of items.…”

Section: Group Recommendation Systems Grsmentioning

confidence: 99%

Group Recommendation Systems Based on External Social‐Trust Networks

Fang

Jiang

et al. 2018

Wireless Communications and Mobile Computing

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With the development of social networks and online mobile communities, group recommendation systems support users' interaction with similar interests or purposes with others. We often provide some advices to the close friends, such as listening to favorite music and sharing favorite dishes. However, users' personalities have been ignored by the traditional group recommendation systems while the majority is satisfied. In this paper, a method of group recommendation based on external socialtrust networks is proposed, which builds a group profile by analyzing not only users' preferences, but also the social relationships between members inside and outside of the group. We employ the users' degree of disagreement to adjust group preference rating by external information of social-trust network. Moreover, having a discussion about different social network utilization ratio, we proposed a method to work for smaller group size. The experimental results show that the proposed method has consistently higher precision and leads to satisfactory recommendations for groups.

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Section: Group Recommendation Systems Grsmentioning

confidence: 99%

Group Recommendation Systems Based on External Social‐Trust Networks

Fang

Jiang

et al. 2018

Wireless Communications and Mobile Computing

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“…CBF approaches and CF algorithms have both been used fairly successfully to build recommendation systems in various domains [11][12][13]16]. However, as described above, they suffer from the cold-start problem in one form or another.…”

Section: Related Workmentioning

confidence: 99%

A Content-Boosted Collaborative Filtering Algorithm for Personalized Training in Interpretation of Radiological Imaging

2014

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Devising a method that can select cases based on the performance levels of trainees and the characteristics of cases is essential for developing a personalized training program in radiology education. In this paper, we propose a novel hybrid prediction algorithm called content-boosted collaborative filtering (CBCF) to predict the difficulty level of each case for each trainee. The CBCF utilizes a content-based filtering (CBF) method to enhance existing trainee-case ratings data and then provides final predictions through a collaborative filtering (CF) algorithm. The CBCF algorithm incorporates the advantages of both CBF and CF, while not inheriting the disadvantages of either. The CBCF method is compared with the pure CBF and pure CF approaches using three datasets. The experimental data are then evaluated in terms of the MAE metric. Our experimental results show that the CBCF outperforms the pure CBF and CF methods by 13.33 and 12.17 %, respectively, in terms of prediction precision. This also suggests that the CBCF can be used in the development of personalized training systems in radiology education.

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“…This method, while exploring previous check-ins across users, does not assess similarity between users in predicting future locations, an aspect that our research suggests is beneficial. Traditional work in collaborative filtering (e.g., Amazon recommendations) has also focused on measuring user similarity, but typically concentrates on "structured" data such as numerical (star) ratings [11,3].…”

Section: Related Workmentioning

confidence: 99%

A Thematic Approach to User Similarity Built on Geosocial Check-ins

McKenzie

Adams

Janowicz

2013

Geographic Information Science at the Heart of Europe

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Abstract. Computing user similarity is key for personalized locationbased recommender systems and geographic information retrieval. So far, most existing work has focused on structured or semi-structured data to establish such measures. In this work, we propose topic modeling to exploit sparse, unstructured data, e.g., tips and reviews, as an additional feature to compute user similarity. Our model employs diagnosticity weighting based on the entropy of topics in order to assess the role of commonalities and variabilities between similar users. Finally, we offer a validation technique and results using data from the locationbased social network Foursquare.

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Amazon.com recommendations: item-to-item collaborative filtering

Cited by 4,630 publications

References 6 publications

Group Recommendation Systems Based on External Social‐Trust Networks

Group Recommendation Systems Based on External Social‐Trust Networks

A Content-Boosted Collaborative Filtering Algorithm for Personalized Training in Interpretation of Radiological Imaging

A Thematic Approach to User Similarity Built on Geosocial Check-ins

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