Mining Multiple Web Data Sources for Integrated Informative Querying and Recommendation

推荐系统利用一个数据源上的用户偏好和用户需求数据来进行关于产品或用户的推荐，并且可以扩展到多个数据源（MDS），其中评级从用户/项目行为的顺序模式计算，用于比较查询和更高的推荐准确性。快速增长的在线电子商务零售业对加拿大的经济增长至关重要。越来越多的人选择从沃尔玛和加拿大轮胎等电子商店在线购物。购物者想要更好的交易，而零售商想要保持竞争力，盈利和增加销售。购物者和零售商可以从使用诸如价格、用户意见等属性的产品的比较查询和推荐中受益。产品特征或用户行为的顺序模式可以用于总结产品类别和用户对产品的评级，以便进行推荐。现有的序列模式挖掘技术在单个表上发现频繁模式，并且不能回答可能通过外键属性相关的多个表上的复杂模式查询，以使得能够分析来自MDS的数据。 需要开发用于挖掘模式组的方法（例如，盈利），从MDS集成模式，清理和仓储历史Web数据，建议和处理来自源的不断变化的数据。这些方法可以帮助用户做出良好的购买选择，并帮助零售商留住客户。因此，所提出的研究计划的目标是通过定义新的算法来对MDS进行比较查询、挖掘和推荐，以（i）挖掘用于回答复杂用户查询的相关模式，（ii）推荐和预测MDS上最相关的项目或用户组。要使用的研究方法包括将MDS转换为具有外键链接的统一格式的序列模式挖掘，以及定义类似于GSP-join的方法来挖掘更高级别的n项集模式（例如，首先将数据库转换为具有每个事务中每个1项出现的所有连续性的记录，并且使每个频繁模式将其外键属性链接到相关表，并且定义用于挖掘更高级别的n项集模式的方法）。也可以定义非Apriori树状方法的扩展。对于推荐系统，我们计划使用协同过滤（CF）与汇总模式用户/项目矩阵，从MDS的数据（用户，项目，评级）的顺序模式的用户和项目数据建模，使系统更可扩展，更容易理解和回答更复杂的查询与历史，派生和比较数据。 本文提出的改进电子商务中MDS推荐质量的方法是新颖的，可以使加拿大的行业和人们在比较价格、意见和历史数据时受益，所构建的数据分组方法可以用于其他应用领域。

Recommendation systems make use of user-preferences and user-requirements data on one data source to make recommendations about products or users and can be extended to multiple data sources (MDSs) with ratings computed from sequential patterns of user/item behaviors for comparative querying and higher recommendation accuracy. Fast growing online E-commerce retail industry is essential to Canada's economic growth. More people are choosing online purchases from E-stores like Walmart and Canadian Tire. Shoppers want better deals while retailers want to remain competitive, profitable and increase sales. Shoppers and retailers can benefit from comparative querying and recommendation of products using attributes such as prices, user opinions. Sequential patterns of product features or user behavior can be used to summarize classes of products and their ratings by users for the purposes of recommendations. Existing sequential pattern mining techniques find frequent patterns on single tables and are not able to answer complex pattern queries on multiple tables that may be related through foreign key attributes to enable analyzing data from MDSs. There is need to develop methods for mining groups of patterns (eg. profitable), integrating patterns from MDSs, cleaning and warehousing historical web data, recommendations and handling evolving data from sources. These methods can help users make good purchase choices and help retailers retain customers.******Thus, the goal of the proposed research program is to do comparative querying, mining and recommendations on MDSs by defining new algorithms to (i) mine relevant patterns for answering complex user queries, (ii) recommend and predict most relevant groups of items or users on MDSs. The research methods to be used include for sequential pattern mining transformation of MDSs into a uniform format with foreign key link, and defining methods similar to the GSP-join for mining higher level n-itemset patterns (eg., first transforming the database to have a record of all subsequences where each 1-item occurs in each transaction and having each frequent pattern carry its foreign key attribute link to a related table, and defining methods for mining higher level n-itemset patterns). Extensions of non-Apriori tree-like approaches can also be defined. For recommendation systems, we plan to model user and item data as sequential patterns of data (users, items, ratings) from MDSs using Collaborative filtering (CF) with summarized pattern user/item matrix, making system more extendable, understandable and answering more complex queries with historical, derived and comparative data. The proposed methods for improving the quality of MDSs recommendations in E-Commerce are novel, can benefit Canada's industries and people for comparing prices, opinions and historical data, the constructed data grouping methods can be used in other application domains.