Mining Web Data Sources for Integrated Informative Querying and Recommendation

推荐系统利用用户偏好、来自Web源的用户和项目数据来做出关于产品或用户的推荐。协同过滤是推荐系统的一种方法，它使用用户对用户项目评级矩阵中的产品的评级来推荐其他产品。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，因为越来越多的人选择在线购物以获得便利和更好的交易。零售商希望保持竞争力、盈利能力并增加销售额。推荐系统通过推荐所需的、新的和多样化的物品来帮助用户科普太多的产品选择。 物品i对用户u的效用（评级），表示为R（u，i），可以从交易数据预测。序列模式挖掘（SPM）可以将顾客购买行为的模型学习为序列模式，并将序列模式转换为效用函数R（u，i），用于推荐系统的协同过滤算法，以获得更有效、更多样、更准确的结果。** 现有的推荐系统不会从历史或点击流数据中学习客户购买行为的顺序模式。因此，所提出的研究计划的目标是使用序列模式挖掘或结合其他挖掘方法对推荐系统输入数据源（例如。历史购买和点击流数据）来发现更丰富的客户兴趣，例如购买的顺序模式，以便提高推荐系统的准确性和多样性。算法将被开发用于发现、整合和使用挖掘的购买顺序模式，以提高i）产品的用户评级的质量，ii）以前很大程度上稀疏的评级的数量，iii）扩展用于单个表的顺序模式挖掘技术，以处理通过外键属性相关的多个数据源的综合查询和挖掘。使用的方法包括使用SPM算法（例如，GSP）发现顺序（例如。罕见的，罕见的购买。序列模式然后被用作用户购买项目向量以从项目组导出单个真实的评级值。为了整合挖掘结果，我们将定义Apriori类（例如，GSP连接技术）和非Apriori类型技术（例如，基于树的），用于通过首先将原始表转换成元组模式来查找相关多个数据源中的顺序模式，元组模式包含子序列，子序列具有它们出现的外键属性，以便链接顺序模式的记录出现。这项研究是新颖的，因为没有现有的工作探索这种方法。它可以提高项目效用评级函数值的质量和数量，使结果更加准确和多样化，带来更高的销售额、用户便利性和忠诚度。外键链接模式是一种新颖的模式挖掘方法，有助于SPM的基本解决方案，提高系统性能，整合挖掘出的模式。

Recommendation systems make use of user preferences, user and item data from web sources, to make recommendations about products or users. Collaborative filtering is a method of recommendation systems that uses user ratings for products in a user-item rating matrix to recommend additional products. Fast growing online E-commerce retail industry is essential to Canada's economic growth as more people choose online purchases for convenience and better deals. Retailers want to remain competitive, profitable and increase sales. Recommendation systems help users cope with too many product choices by recommending needed, new and diverse items. The utility (rating) of an item i to a user u, expressed as R(u, i) can be predicted from transaction data. Sequential pattern mining (SPM) can learn a model of customer purchase behaviour as sequential patterns which can be converted to utility function R(u, i) for a recommendation system collaborative filtering algorithm for more effective, diverse and accurate results. ******Existing recommendation systems do not learn sequential patterns of customer purchase behaviour from historical or click stream data. Thus, the goal of the proposed research program is to use sequential pattern mining or in conjunction with other mining methods on recommendation system input data sources (eg. historical purchase and click-stream data) to discover richer customer interests, such as sequential patterns of purchases so as to improve recommendation system accuracy and diversity. Algorithms will be developed for discovering, integrating and using mined sequential patterns of purchases to improve i) the quality of user ratings of products, ii) the quantity of ratings previously largely sparse, iii) to extend sequential pattern mining techniques for single table to handle integrative querying and mining of multiple data sources related through foreign key attributes. Methods to be used include pre-processing relevant E-Commerce data sources with SPM algorithms (eg. GSP) to discover sequential (eg. frequent, rare) of purchases. Sequential patterns are then used as the user purchase item vectors to derive a single real rating value from the groups of items. To integrate mining results, we will define both Apriori-like (eg. GSP join techniques) and non-Apriori type techniques (eg. tree based) for finding sequential patterns in related multiple data sources by first transforming the original tables into tuple patterns that contain sub-sequences with foreign key attributes where they occurred, so as to link record occurrences of sequential patterns.******The research is novel as no existing work explored this approach. It can improve the quality and quantity of item utility rating function values for more accurate and diverse results, lead to higher sales, user convenience and loyalty. The foreign key linked patterns is novel, contributes to fundamental SPM solution, improved system performance, integrating mined patterns.*****