一种考虑用户兴趣转移特征的协同预测模型

A collaborative prediction model for user interest shift feature

摘要:

目前，大多数预测模型使用用户属性或社交关系信息来优化预测结果，然而真实系统中用户的属性或社交关系信息往往很难获得，或者取得的是虚假信息，从而导致用户行为表达不准确或模型不具有普适性。另外，几乎所有使用用户特征的模型仅考虑用户兴趣本身的度量，而忽视兴趣的变化这一重要特征。为此，本文提出一种考虑用户兴趣转移特征的协同预测模型。该模型根据用户连续行为序列构建用户行为演变网络和用户兴趣转移特征，利用用户兴趣转移特征计算用户相似性，进而搜索最近邻集合，利用用户行为演变网络筛选候选集，最后设计最频繁项提取算法来产生预测结果，从而构建用户行为的预测模型。在真实的新闻浏览日志、交互式网络电视视频访问日志和微软服务器日志上的实验表明该预测模型是有效的。

Abstract:

Recently, most of prediction models employ basic attributes of users or social relationships as the original features to optimize prediction results. Nevertheless, in the real systems, it’s highly difficult to acquire attributes and social information of users, which always lack of sufficient accuracy, so that user behavior couldn’t be expressed accurately as well as prediction models couldn’t be applied widely. Additionally, almost the whole models employing user features focus on measurement of interests of user itself, while ignoring the feature of the change of user interests. Based on the above considerations, we propose a collaborative prediction model based on transfer features of user interests. The model constructs network evolution of user behavior and transfer features of user interests according to the sequences of consistent user behavior, of which the former is used to compute similarity of users to retrieve nearest neighbor sets while the latter is applied to filter candidate sets. With the application of a news browsing log, as well as an internet protocol television access log and a microsoft server log, experiments indicate the effectiveness and practicability of our prediction model.