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Weighted Bipartite Graph Model for Recommender System Using Entropy Based Similarity Measure

机译:基于熵的相似度量的推荐系统加权二分图模型

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Collaborative filtering technique is widely adopted by researchers to generate quality recommendations. Constant efforts are being made by the researchers to generate quality recommendations thus satisfying and retaining the user. This work is an effort to generate quality recommendations by proposing a collaborative filtering approach. The proposed work models the sparse rating data as a weighted bipartite graph which represents data flexibly and exploits the graph properties to generate recommendations. In the proposed work user similarity is formulated as measure of entropy and cosine similarity which takes into account the relative difference between the ratings. Performance of the proposed approach is compared with the traditional collaborative filtering technique using Precision, Recall and F-Measure. Experiments were conducted on public and private datasets namely MovieLens and News dataset respectively. Results indicate that the performance of the proposed approach outperforms the traditional collaborative filtering approach.
机译:研究人员广泛采用协作过滤技术,以产生质量建议。研究人员正在制定不断的努力,从而产生质量建议,从而令人满意和保留用户。这项工作是通过提出协作过滤方法来产生质量建议的努力。该建议的工作模拟稀疏额定数据作为加权二分图,这是灵活表示数据的,并利用图形属性来生成建议。在拟议的工作中,用户相似性被制定为熵和余弦相似度的衡量标准,这考虑了评级之间的相对差异。将所提出的方法的性能与使用精密,召回和F测量的传统协作过滤技术进行比较。在公共和私有数据集上进行实验即可分别在Movielens和News DataSet上进行。结果表明,所提出的方法的性能优于传统的协作过滤方法。

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