Mining Web Data Sources for Integrated Informative Querying and Recommendation

推荐系统利用来自网络资源的用户偏好、用户和项目数据，对产品或用户进行推荐。协同过滤是推荐系统的一种方法，它使用用户对用户-物品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，以获得方便和更好的交易。零售商希望保持竞争力、盈利能力并增加销售额。推荐系统通过推荐用户需要的、新的和多样化的产品来帮助用户处理太多的产品选择。物品i对用户u的效用（评级），表示为R(u, i)，可以从事务数据中预测。顺序模式挖掘（Sequential pattern mining， SPM）可以将客户购买行为模型学习为顺序模式，并将其转换为效用函数R(u, i)，用于推荐系统协同过滤算法，从而获得更有效、更多样化和更准确的结果。现有的推荐系统不能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顺序模式挖掘或与其他挖掘方法结合在推荐系统输入数据源上(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，从而提高推荐系统的准确性和多样性。将开发用于发现、集成和使用挖掘的购买顺序模式的算法，以提高i)用户对产品评级的质量，ii)以前基本上稀疏的评级数量，iii)扩展单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集成查询和挖掘。使用的方法包括使用SPM算法预处理相关的电子商务数据源(例如。GSP)来发现顺序的(例如；频繁的，罕见的)购买。然后使用顺序模式作为用户购买商品向量，从商品组中获得单个真实评级值。为了整合挖掘结果，我们将定义类apriori(例如：GSP连接技术)和非apriori类型技术(例如。基于树的)，通过首先将原始表转换为包含子序列的元组模式，从而在相关的多个数据源中查找顺序模式，这些子序列在它们出现的地方具有外键属性，从而链接顺序模式的出现记录。这项研究是新颖的，因为没有现有的工作探索这种方法。它可以提高物品效用评级函数值的质量和数量，使结果更加准确和多样化，从而提高销售额，用户便利性和忠诚度。外键链接模式是一种新颖的方法，有助于实现基本的SPM解决方案，提高系统性能，集成已挖掘的模式。

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.