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Improving Recommendation Accuracy using Cross-domain Similarity

机译：使用跨域相似性提高推荐准确性

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摘要

The accuracy of collaborative filtering based recommendation system depends on sufficient rating information of the target user and his/her neighbors for similar items. In a real scenario where a huge number of users and items exist, there may be a possibility of very less (high sparsity) or no (cold start problem) rating information in the rating dataset, which degrades the recommendation accuracy significantly. This opens up a scope for improvement in the prediction accuracy of the collaborative filtering based recommender system. In this paper, if the target user does not find k similar neighbors in a particular domain, the proposed algorithm utilizes the rating information of that user from other domains, if available. This not only reduces the sparsity in the rating dataset but also solves the cold start problem. Additionally, the modified similarity measure of the proposed approach not only considers the high ratings but also the low ratings which makes the recommendation more personalised. Overall, the proposed approach improves the recommendation accuracy to a great extent, which has been been evident from the accuracy measures (e.g., MAE and RMSE) of the comparative analysis of the proposed algorithm and other prevalent collaborative filtering methods, investigated on the Amazon dataset.
机译：基于协作过滤的建议系的准确性取决于目标用户的充分评级信息和他/她/她的邻居的类似物品。在存在一个巨大的用户和项目数的实际情况，有可能是非常少的（高稀疏）的可能性或评级数据集，显著降低了推荐精度没有（冷启动问题）的评级信息。这为基于协作滤波的推荐系统的预测精度提供了改进的范围。在本文中，如果目标用户在特定域中找不到K类似的邻居，则该算法利用来自其他域的该用户的评级信息（如果有）。这不仅减少了评级数据集中的稀疏性，而且还解决了冷启动问题。此外，所提出的方法的修改相似度尺寸不仅考虑了高评级，而且使得这些建议更加个性化的低评级。总体而言，该方法提高了推荐准确度在很大程度上，这已是显而易见的，从所提出的算法和其他流行协同过滤方法的比较分析的准确性度量（例如，MAE和RMSE），研究了在亚马逊数据集。

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