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对产品或用户进行推荐，并且可以扩展到多个数据源（mds），并根据用户/项目行为的顺序模式计算评级，以进行比较查询和更高的推荐准确性。快速发展的在线电子商务零售业对加拿大的经济增长至关重要。越来越多的人选择在沃尔玛和加拿大轮胎等电子商店网上购物。消费者想要更好的交易，而零售商想要保持竞争力、利润和增加销售额。购物者和零售商可以从使用价格、用户意见等属性的比较查询和产品推荐中获益。产品特性或用户行为的顺序模式可用于总结产品类别及其用户评分，以便进行推荐。现有的顺序模式挖掘技术只能在单个表上找到频繁的模式，而不能回答多个表上的复杂模式查询，这些表可能通过外键属性相关联，从而能够分析来自mds的数据。需要开发挖掘模式组的方法(例如。盈利)，整合mds模式，清理和存储历史网络数据，建议和处理来自数据源的不断发展的数据。这些方法可以帮助用户做出正确的购买选择，帮助零售商留住顾客。******因此，所提议的研究计划的目标是通过定义新的算法(i)挖掘相关模式来回答复杂的用户查询，（ii）推荐和预测mds上最相关的项目或用户组，从而对mds进行比较查询、挖掘和推荐。研究方法包括对mds进行顺序模式挖掘，将mds转换为具有外键链接的统一格式，并定义类似于GSP-join的方法来挖掘更高级别的n项集模式(如：（首先将数据库转换为具有所有子序列的记录，其中每个1项在每个事务中出现，并使每个频繁模式携带其外键属性链接到相关表，并定义挖掘更高级别n项集模式的方法）。也可以定义非apriori类树方法的扩展。对于推荐系统，我们计划将用户和项目数据建模为来自mds的数据（用户、项目、评级）的顺序模式，使用具有汇总模式用户/项目矩阵的协同过滤（CF），使系统更具可扩展性、可理解性，并使用历史、派生和比较数据回答更复杂的查询。本文提出的提高电子商务中mds推荐质量的方法是新颖的，可以为加拿大的行业和人民提供比较价格、意见和历史数据的方法，所构建的数据分组方法可用于其他应用领域。

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.