Recommending Best Products from E-commerce Purchase History and User Click Behavior Data

E-commerce collaborative filtering recommendation systems, the main input data of user-item rating matrix is a binary purchase data showing only what items a user has purchased recently. This matrix is usually sparse and does not provide a lot of information about customer purchases or product clickstream behavior (eg., clicks, basket placement, and purchase) history, which possibly can improve product recommendations accuracy. Existing recommendation systems in E-commerce with clickstream data include those referred in this thesis as Kim05Rec, Kim11Rec, and Chen13Rec. Kim05Rec forms a decision tree on click behavior attributes such as search type and visit times, discovers the possibility of a user putting products into the basket and uses the information to enrich the user-item rating matrix. If a user clicked a product, Kim11Rec then finds the associated products for it in three stages such as click, basket and purchase, uses the lift value from these stages and calculates a score, it then uses the score to make recommendations. Chen13Rec measures the similarity of users on their category click patterns such as click sequences, click times and visit duration; it then can use the similarity to enhance the collaborative filtering algorithm. However, the similarity between click sequences in sessions can apply to the purchases to some extent, especially for sessions without purchases, this will be able to predict purchases for those session users. But the existing systems have not integrated it, or the historical purchases which shows more than whether or not a user has purchased a product before. In this thesis, we propose HPCRec (Historical Purchase with Clickstream based Recommendation System) to enrich the ratings matrix from both quantity and quality aspects. HPCRec firstly forms a normalized rating-matrix with higher quality ratings from historical purchases, then mines consequential bond between clicks and purchases with weighted frequencies where the weights are similarities between sessions, but rating quantity is better by integrating this information. The experimental results show that our approach HPCRec is more accurate than these existing methods, HPCRec is also capable of handling infrequent cases whereas the existing methods can not

Comparison of edit history clustering techniques for spatial hypertext

History mechanisms available in hypertext systems allow access to past user interactions with the system. This helps users evaluate past work and learn from past activity. It also allows systems identify usage patterns and potentially predict behaviors with the system. Thus, recording history is useful to both the system and the user. Various tools and techniques have been developed to group and annotate history in Visual Knowledge Builder (VKB). But the problem with these tools is that the operations are performed manually. For a large VKB history growing over a long period of time, performing grouping operations using such tools is difficult and time consuming. This thesis examines various methods to analyze VKB history in order to automatically group/cluster all the user events in this history. In this thesis, three different approaches are compared. The first approach is a pattern matching approach identifying repeated patterns of edit events in the history. The second approach is a rule-based approach that uses simple rules, such as group all consecutive events on a single object. The third approach uses hierarchical agglomerative clustering (HAC) where edits are grouped based on a function of edit time and edit location. The contributions of this thesis work are: (a) developing tools to automatically cluster large VKB history using these approaches, (b) analyzing performance of each approach in order to determine their relative strengths and weaknesses, and (c) answering the question, how well do the automatic clustering approaches perform by comparing the results obtained from this automatic tool with that obtained from the manual grouping performed by actual users on a same set of VKB history. Results obtained from this thesis work show that the rule-based approach performs the best in that it best matches human-defined groups and generates the fewest number of groups. The hierarchic agglomerative clustering approach is in between the other two approaches with regards to identifying human-defined groups. The pattern-matching approach generates many potential groups but only a few matches with those generated by actual VKB users