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首页> 外文期刊>Eurasip Journal on Wireless Communications and Networking >Recommendation algorithm based on user score probability and project type
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Recommendation algorithm based on user score probability and project type

机译：基于用户评分概率和项目类型的推荐算法

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Abstract The interaction and sharing of data based on network users make network information overexpanded, and “information overload” has become a difficult problem for everyone. The information filtering technology based on recommendation could dig out the needs and hobbies of users from the historical behavior, historical data, and social network and filter out useful resource for users in accordance with the needs and hobbies from the accumulation of information resource. Collaborative filtering is one of the core technologies in the recommendation system and is also the most widely used and most effective recommendation algorithm. In this paper, we study the accuracy and the data sparsity problems of recommendation algorithm. On the basis of the conventional algorithm, we combine the user score probability and take the commodity type into consideration when calculating similarity. The algorithm based on user score probability and project type (UPCF) is proposed, and the experimental data set from the recommendation system is used to validate and analyze data. The experimental results show that the UPCF algorithm alleviates the sparsity of data to a certain extent and has better performance than the conventional algorithms.
机译：摘要基于网络用户的数据交互和共享使得网络信息过高，“信息过载”为每个人都成为一个难题。基于推荐的信息过滤技术可以根据信息资源累积的需求和爱好挖掘用户来自历史行为，历史数据和社交网络的用户需求和爱好，并为用户筛选用户的有用资源。协作过滤是推荐系统中的核心技术之一，也是最广泛使用，最有效的推荐算法。在本文中，我们研究了推荐算法的准确性和数据稀疏问题。在传统算法的基础上，我们将用户得分概率结合起来，并在计算相似度时考虑商品类型。提出了基于用户评分概率和项目类型（UPCF）的算法，并使用来自推荐系统的实验数据来验证和分析数据。实验结果表明，UPCF算法在一定程度上缓解了数据的稀疏性，并且具有比传统算法更好的性能。

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