Mining Web Data Sources for Integrated Informative Querying and Recommendation

推荐系统可以利用来自Web源的用户偏好、用户和项目数据，来推荐关于产品或用户的信息。协作过滤是推荐系统的一种方法，它使用用户-项目评级矩阵中的产品的用户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，因为越来越多的人为了方便和更好的交易而选择在线购物。零售商希望保持竞争力、盈利和增加销售额。推荐系统通过向用户推荐所需的、新的和多样化的商品来帮助用户应对太多的产品选择。可以从交易数据预测商品i对用户u的效用(评级)，表示为uR(u，i)。序列模式挖掘(SPM)可以学习顾客购买行为的模型作为序列模式，这些序列模式可以转换为效用函数R(u，i)，用于推荐系统的协同过滤算法，以获得更有效、更多样和更准确的结果。现有的推荐系统不能从历史数据或点击流数据中学习客户购买行为的顺序模式。因此，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)，以发现更丰富的客户兴趣，如购买的顺序模式，从而提高推荐系统的准确性和多样性。将开发算法来发现、集成和使用挖掘的购买序列模式，以提高i)产品的用户评级的质量，ii)以前基本上稀疏的评级的数量，iii)扩展单表的序列模式挖掘技术，以处理通过外键属性关联的多个数据源的综合查询和挖掘。使用的方法包括使用SPM算法对相关电子商务数据源进行预处理(例如，GSP)以发现序列(例如频繁的，罕见的)购买。然后，序列模式被用作用户购买项目向量，以从项目组中获得单个真实评价值。为了集成挖掘结果，我们将定义两个类Apriori(例如，GSP连接技术)和非关联类型技术(例如，基于树)，用于通过首先将原始表转换成包含具有外键属性的子序列的元组模式来在相关的多个数据源中找到序列模式，以便链接序列模式的记录出现。这项研究是新颖的，因为目前还没有研究这一方法的工作。它可以提高项目效用评分函数值的质和量，使结果更加准确和多样，从而带来更高的销售额、用户便利性和忠诚度。外键链接模式新颖，有助于从根本上解决SPM问题，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.