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

New Approach for Market Intelligence Using Artificial and Computational Intelligence

Small and medium sized retailers are central to the private sector and a vital contributor to economic growth, but often they face enormous challenges in unleashing their full potential. Financial pitfalls, lack of adequate access to markets, and difficulties in exploiting technology have prevented them from achieving optimal productivity. Market Intelligence (MI) is the knowledge extracted from numerous internal and external data sources, aimed at providing a holistic view of the state of the market and influence marketing related decision-making processes in real-time. A related, burgeoning phenomenon and crucial topic in the field of marketing is Artificial Intelligence (AI) that entails fundamental changes to the skillssets marketers require. A vast amount of knowledge is stored in retailers’ point-of-sales databases. The format of this data often makes the knowledge they store hard to access and identify. As a powerful AI technique, Association Rules Mining helps to identify frequently associated patterns stored in large databases to predict customers’ shopping journeys. Consequently, the method has emerged as the key driver of cross-selling and upselling in the retail industry. At the core of this approach is the Market Basket Analysis that captures knowledge from heterogeneous customer shopping patterns and examines the effects of marketing initiatives. Apriori, that enumerates frequent itemsets purchased together (as market baskets), is the central algorithm in the analysis process. Problems occur, as Apriori lacks computational speed and has weaknesses in providing intelligent decision support. With the growth of simultaneous database scans, the computation cost increases and results in dramatically decreasing performance. Moreover, there are shortages in decision support, especially in the methods of finding rarely occurring events and identifying the brand trending popularity before it peaks. As the objective of this research is to find intelligent ways to assist small and medium sized retailers grow with MI strategy, we demonstrate the effects of AI, with algorithms in data preprocessing, market segmentation, and finding market trends. We show with a sales database of a small, local retailer how our Åbo algorithm increases mining performance and intelligence, as well as how it helps to extract valuable marketing insights to assess demand dynamics and product popularity trends. We also show how this results in commercial advantage and tangible return on investment. Additionally, an enhanced normal distribution method assists data pre-processing and helps to explore different types of potential anomalies.Små och medelstora detaljhandlare är centrala aktörer i den privata sektorn och bidrar starkt till den ekonomiska tillväxten, men de möter ofta enorma utmaningar i att uppnå sin fulla potential. Finansiella svårigheter, brist på marknadstillträde och svårigheter att utnyttja teknologi har ofta hindrat dem från att nå optimal produktivitet. Marknadsintelligens (MI) består av kunskap som samlats in från olika interna externa källor av data och som syftar till att erbjuda en helhetssyn av marknadsläget samt möjliggöra beslutsfattande i realtid. Ett relaterat och växande fenomen, samt ett viktigt tema inom marknadsföring är artificiell intelligens (AI) som ställer nya krav på marknadsförarnas färdigheter. Enorma mängder kunskap finns sparade i databaser av transaktioner samlade från detaljhandlarnas försäljningsplatser. Ändå är formatet på dessa data ofta sådant att det inte är lätt att tillgå och utnyttja kunskapen. Som AI-verktyg erbjuder affinitetsanalys en effektiv teknik för att identifiera upprepade mönster som statistiska associationer i data lagrade i stora försäljningsdatabaser. De hittade mönstren kan sedan utnyttjas som regler som förutser kundernas köpbeteende. I detaljhandel har affinitetsanalys blivit en nyckelfaktor bakom kors- och uppförsäljning. Som den centrala metoden i denna process fungerar marknadskorgsanalys som fångar upp kunskap från de heterogena köpbeteendena i data och hjälper till att utreda hur effektiva marknadsföringsplaner är. Apriori, som räknar upp de vanligt förekommande produktkombinationerna som köps tillsammans (marknadskorgen), är den centrala algoritmen i analysprocessen. Trots detta har Apriori brister som algoritm gällande låg beräkningshastighet och svag intelligens. När antalet parallella databassökningar stiger, ökar också beräkningskostnaden, vilket har negativa effekter på prestanda. Dessutom finns det brister i beslutstödet, speciellt gällande metoder att hitta sällan förekommande produktkombinationer, och i att identifiera ökande popularitet av varumärken från trenddata och utnyttja det innan det når sin höjdpunkt. Eftersom målet för denna forskning är att hjälpa små och medelstora detaljhandlare att växa med hjälp av MI-strategier, demonstreras effekter av AI med hjälp av algoritmer i förberedelsen av data, marknadssegmentering och trendanalys. Med hjälp av försäljningsdata från en liten, lokal detaljhandlare visar vi hur Åbo-algoritmen ökar prestanda och intelligens i datautvinningsprocessen och hjälper till att avslöja värdefulla insikter för marknadsföring, framför allt gällande dynamiken i efterfrågan och trender i populariteten av produkterna. Ytterligare visas hur detta resulterar i kommersiella fördelar och konkret avkastning på investering. Dessutom hjälper den utvidgade normalfördelningsmetoden i förberedelsen av data och med att hitta olika slags anomalier

Survey of users of earth resources remote sensing data

A user survey was conducted to determine current earth resources survey (ERS) data use/user status and recommendations for strengthening use. Only high-altitude aircraft and satellite (primarily LANDSAT) data were included. Emphasis was placed on the private sector/industrial user. Objectives of the survey included: who is using ERS data, how they are using the data, the relative value of current data use as well as obtaining user views as to possible ways of strengthening future ERS data use. The survey results are documented and should provide relevant decision making information for developing future programs of maximum benefit to all end users of satellite ERS data

The relevance of ERTS-1 data to the state of Ohio

There are no author-identified significant results in this report

PRODUCT RECOMMENDATION BY LINKING UP ONLINE DIGITAL MEDIA WITH ELECTRONIC COMMERCE

We deliver several algorithms to resolve this innovatory mining proposition through three phases: preprocessing to descent probabilistic topics and recognize sessions for various users, produce all of the STP candidates with (expected) back values for every user by archetype-growth, make up one's mind on URSTPs by looking into making user-aware rarity analysis on derived STPs. Poor deterministic data, an extensive survey is effectual. The idea support is easily the most popular measure for rate the regularity of the consecutive pattern and is understood to be the amount or apportion of information result that contains the pattern within the target databank. The learned patterns aren't always absorbing for the purpose, because individual’s rare but symbol patterns express personalized and anomalous behaviors are plum ask of low supports. We advise a framework to pragmatically explain this delivery, and style corresponding algorithms to assist it. Initially, we give preprocessing procedures with heuristic means of exposed essence and assize identification. This method could be observed as consequence duplicate between your buy topics specified by the STP and also the probabilistic topics appear within the purchased muniment owned by a particular session. The outcomes indicate our near can certainly capture personalized behaviors of Online users and express them within an understandable road

An Evaluation of the Use of Diversity to Improve the Accuracy of Predicted Ratings in Recommender Systems

The diversity; versus accuracy trade off, has become an important area of research within recommender systems as online retailers attempt to better serve their customers and gain a competitive advantage through an improved customer experience. This dissertation attempted to evaluate the use of diversity measures in predictive models as a means of improving predicted ratings. Research literature outlines a number of influencing factors such as personality, taste, mood and social networks in addition to approaches to the diversity challenge post recommendation. A number of models were applied included DecisionStump, Linear Regression, J48 Decision Tree and Naive Bayes. Various evaluation metrics such as precision, recall, ROC area, mean squared error and correlation coefficient were used to evaluate the model types. The results were below a benchmark selected during the literature review. The experiment did not demonstrate that diversity measures as inputs improve the accuracy of predicted ratings. However, the evaluation results for the model without diversity measures were low also and comparable to those with diversity indicating that further research in this area may be worthwhile. While the experiment conducted did not clearly demonstrate that the inclusion of diversity measures as inputs improve the accuracy of predicted ratings, approaches to data extraction, pre-processing, and model selection could inform further research. Areas of further research identified within this paper may also add value for those interested in this topic

ECONOMICALLY ELECTIVE INITIATIVE USERS FOR GUIDE INFLATION MOD GENERAL NETWORKS

Many algorithms are used to solve the digital uncertainty insurance in three ways: before the possible possibilities, breaks and deadlines for different users, move all STP competitors with the expected improvements) for all users through the development of archetypes, in the URSTP, testing the guarantee sponsorship service in the STPs that we have analyzed. The poor database is poor, so many studies are valid. The most reliable solution is the most important for the most accurate and constant accuracy and we appreciate that it is the quantity or distribution of information that contains examples in the business bank. Educational learning is not always the purpose, since people have a limited degree, but personal symbols and behaviors with less support are sought. We propose a framework to reflect this recommendation and the type of algorithm needed to help. Previously, recommendations were given with convincing ways of promoting inappropriate promotion and identification. This method can be considered as a copy of your purchases made by STP and possible notes that appear within the applications purchased over a period of time. The results that we show nearby may be to explore the personal habits of Internet users and show them on the family path

PRESSURE DISCOVERY BASED ON CONNECTING IN SOCIAL NETWORK

We provide several algorithms to solve the mining proposal three new sections: processed down probabilistic issues and see a number of sessions and users, we produce everything expected (expected) STP option prices in the user's knee of growth, we decided in URSTP by user analysis known as STP rare products. Selected data are false, large-scale successful surveys. Easily support the most famous to qualify into a regular pattern and is defined as a quantity or distribution as a result of the information contained in the target pattern in the bank statements. Learning patterns are not always absorbent for this purpose, because patterns are rare individually but people express their behavior and traditions because of the abnormal low platform. We suggest a diagram describing the problem in a pragmatic way and the design that the algorithm shares in the help. Initially, pre-treatment processes offer substantial and open risk. This may be a return to purchase a copy of the topics identified by STP and the probability of purchased subjects that appear in the way that a particular course is owned. The results show that our environment can soon take behavior and concepts that are easy to use in a meaningful way