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Lazy collaborative filtering with dynamic neighborhoods

机译：懒惰与动态协同过滤社区

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Purpose The purpose of this paper is to address the scalability issue and produce high-quality recommendation that best matches the users current preference in the dynamically growing datasets in the context of memory-based collaborative filtering methods using temporal information. Design/methodology/approach The proposed method is formalized as time-dependent collaborative filtering method. For each item, a set of influential neighbors is identified by using the truncated version of similarity computation based on the timestamp. Then, recent n transactions are used to generate the recommendation that reflect the recent preference of the active user. The proposed method, lazy collaborative filtering with dynamic neighborhoods (LCFDN), is further scaled up by implementing in spark using parallel processing paradigm MapReduce. The experiments conducted on MovieLens dataset reveal that LCFDN implemented on MapReduce is more efficient and achieves good performance than the existing methods. Findings The results of the experimental study clearly show that not all ratings provide valuable information. Recommendation system based on LCFDN increases the efficiency of predictions by selecting the most influential neighbors based on the temporal information. The pruning of the recent transactions of the user also addresses the users preference drifts and is more scalable when compared to state-of-art methods. Research limitations/implications In the proposed method, LCFDN, the neighborhood space is dynamically adjusted based on the temporal information. In addition, the LCFDN also determines the users current interest based on the recent preference or purchase details. This method is designed to continuously track the users preference with the growing dataset which makes it suitable to be implemented in the e-commerce industry. Compared with the state-of-art methods, this method provides high-quality recommendation with good efficiency. Originality/value The LCFDN is an extension of collaborative filtering with temporal information used as context. The dynamic nature of data and users preference drifts are addressed in the proposed method by dynamically adapting the neighbors. To improve the scalability, the proposed method is implemented in big data environment using MapReduce. The proposed recommendation system provides greater prediction accuracy than the traditional recommender systems.
机译：目的本文的目的地址和生产高质量的可伸缩性问题建议最佳匹配的用户当前的偏好的动态增长数据集的基于内存的协同过滤使用时间的方法信息。该方法和时间依赖形式化协同过滤方法。一系列有影响力的邻居了使用相似的截断版本基于时间戳的计算。n事务是用于生成建议反映最近的偏好活跃的用户。协同过滤与动态社区(LCFDN),进一步扩大使用并行处理实现的火花范式MapReduce。MovieLens数据集显示,LCFDN实施MapReduce是更高效,做到好性能比现有的方法。实验研究的结果清楚表明,并不是所有的评级提供有价值的信息。增加预测的效率选择最具影响力的邻居的基础上时间信息。最近交易的用户地址用户偏好的雪堆,更具有可伸缩性相比技术发展水平的方法。该方法/限制的影响,LCFDN,附近的空间是动态的调整基于时间信息。此外,LCFDN也决定了用户当前基于最近的兴趣偏好或购买细节。持续跟踪用户的偏好这使得它适合日益增长的数据集在电子商务行业中实现。与先进的方法,这个方法提供高质量和好的建议效率。扩展的协同过滤时间信息作为背景。数据和用户偏好的雪堆的性质该方法通过动态处理适应的邻居。可扩展性,实现该方法在大数据环境下使用MapReduce。提出了推荐系统提供了更大的比传统的预测精度推荐系统。

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