A Recommendation Algorithm from the Object Perspective

Wei Zeng; Ya Fan; Bao Zhuo Zhou; Qing Xian Wang

doi:10.4028/www.scientific.net/AMM.687-691.2664

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691A Recommendation Algorithm from the Object...

A Recommendation Algorithm from the Object Perspective

Abstract:

The purpose of designing recommender systems is to help individual users find relevant information. However, many recommender systems have been facing the challenges of finding niche objects, which users may like but difficult to find due to the lack of sufficient data. In this paper, we propose a recommendation algorithm which takes a niche object as input and outputs a list of users who may be interested it. By this approach, every niche object can be recommended at least one time. Further analysis indicates that those niche objects are usually collected by active users and the owners who are very similar to each other. Therefore, this work has outlined the significant relevance with the challenge, the Long Tail problem, and provided a different perspective to solve it in the field of information filtering.

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Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

2664-2667

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.2664

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Online since:

November 2014

Authors:

Wei Zeng*, Ya Fan, Bao Zhuo Zhou, Qing Xian Wang

Keywords:

Bipartite Network, Collaborative Filtering, Diversity, Recommender System

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* - Corresponding Author

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