增量协同过滤模型研究

项目名称： 增量协同过滤模型研究

项目编号： No.61202347

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 计算机科学学科

项目作者： 罗辛

作者单位： 重庆大学

项目金额： 25万元

中文摘要： 推荐系统能提供"信息找人"的智能服务，从而解决"信息超载"问题。推荐模型是推荐系统中产生推荐结果的核心部件，直接影响推荐系统的性能。目前应用最广的一类推荐模型是协同过滤模型。但绝大多数现有协同过滤模型是静态推荐模型，不具备增量更新能力，应用范围受到很大限制；少数几种增量模型则存在更新方式粗糙、未考虑并行化、缺乏符合自身特点的量化评估方式等缺陷。本项目以深入研究增量协同过滤模型为目标，以K近邻模型和隐向量模型为切入点，展开以下研究：（1）研究增量K近邻模型，包括参数增量更新规则、更新范围控制策略和模型并行化方法；（2）研究增量隐向量模型，包括参数增量更新规则、训练过程优化策略和模型并行化方法；（3）研究增量协同过滤模型聚合，包括同类聚合、异类聚合和分布式聚合；（4）研究增量协同过滤模型的性能评估方式。本项目将力争在增量推荐模型领域取得突破，研究成果能为实现增量推荐系统提供模型支撑和技术手段。

中文关键词： 个性化推荐；协同过滤；增量推荐模型；K 近邻模型；隐向量模型

英文摘要： Recommender systems can solve the problem of 'Information Overload' by associating people with personalized information according to the individual preferences. Inside a recommender system, the recommender is the centerpiece which produces the recommendation results and decides the recommendation performance. In real-world recommender systems, the mostly employed kind of recommenders are based on Collaborative Filtering (CF). However, most of the current CF based recommenders are static recommenders, of which the applicability is restricted in real-world applications due to the lack of ability to perform incremental update. In terms of the proposed incremental CF models, they are all serial models that only implement incremental update roughly. Moreover, there are no specific metrics for evaluating the performance of the incremental recommenders. In this project, we focus in depth on the CF based incremental recommenders. We choose the Neighborhood Based Model (NBM) and the Latent Factor Model (LFM) as the baseline, and focus mainly on the following four issues: (1) the research on the incremental NBM, including the rules of incremental parameter update, the strategies of update scope control and the methods of model parallelizing; (2) the research on the incremental LFM, including the rules of incremental param

英文关键词： Recomender Systems；Collaborative Filtering；Incremental Recommender；KNN Model；Latent Factor Model