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multivariate cluster analysis by gmdh in on-line recommender systems

Agenda. OverviewModern state of art in recommender systemsInductive GMDH and deductive algorithms as tools for data miningModified Combinatorial algorithmAnalogues Complexing algorithmObjective Computer Clusterization algorithmExamples of Applications:Forecasting of the brands launchClusterization of products categoriesEtc.Conclusions.

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multivariate cluster analysis by gmdh in on-line recommender systems

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    1. Multivariate Cluster Analysis by GMDH in On-Line Recommender Systems Gregory IvahnenkoPeerius, London UK

    3. About recommender systems Recommender systems are software applications that aim to support users in their decision-making while interacting with large information spaces. They recommend items of interest to users based on preferences they have expressed, either explicitly or implicitly. The ever-expanding volume and increasing complexity of information on the Web has therefore made such systems essential tools for users. Recommender systems help overcome the information overload problem by exposing users to the most interesting items, and by offering novelty, surprise, and relevance. Recommender technology is hence the central piece of the information seeking puzzle.

    4. Examples of outputs

    5. Recommendation Engine Process

    6. Solutions based on the Business Intelligence platform

    7. Analytic Services Components How do we do it? How do we help companies becoming analytic enterprises? By developing/establishing 3 specific components: First, through data acquisition and integration This is especially important for global analytics because the data is different across countries, but we want to have a common approach and a common output/template. Second, through model management this is not just modeling, but also best practices and templates a systematic approach that can be applied across countries and across operational units. Its important because the approaches may not be identical across countries as the data is different and the market dynamics may differ, but its crucial that the output/template is common and consistent Finally, through analytic reporting and simulation this can take the form of integrated solutions with templates applied to businesses, proprietary applications where you may request the coefficients from our models to be integrated in your existing tool or custom services (specific requests) How do we do it? How do we help companies becoming analytic enterprises? By developing/establishing 3 specific components: First, through data acquisition and integration This is especially important for global analytics because the data is different across countries, but we want to have a common approach and a common output/template. Second, through model management this is not just modeling, but also best practices and templates a systematic approach that can be applied across countries and across operational units. Its important because the approaches may not be identical across countries as the data is different and the market dynamics may differ, but its crucial that the output/template is common and consistent Finally, through analytic reporting and simulation this can take the form of integrated solutions with templates applied to businesses, proprietary applications where you may request the coefficients from our models to be integrated in your existing tool or custom services (specific requests)

    8. MS Business Intelligence (BI) Business Intelligence (BI) is a suite of products that supports data warehousing and data mining: SSAS - SQL Server Analysis Services delivers online analytical processing (OLAP) and data mining functionality for business intelligence applications. SQL Server most often the source store for he OLAP warehouse SSIS SQL Server Integration Services is a set of tools used to prepare the data for loading into OLAP stores SSRS SQL server reporting services are used o display the results to end users via built-in web site

    9. Current marketing problems Price elasticities and price thresholds analysis Marketing mix method, based on regression analysis investigate dependencies of profit from small deflections of price and find price thresholds, after which it will change significantly. Prediction of the new product launches for very small number of observations Long-term forecasting Analysis of relationships of sales from promotions and shoppers demographic data Media analysis optimize media campaigns to maximize profit. Transferable demand approach investigate any possible changes of sales or promotion strategy Clusterization of shopper, store and product characteristics Shopper surveys analyze demographics, attributes of products and shoppers paths in stores Segmentation algorithms select the most demanded attributes of a new products

    10. Data collection

    12. General Problem Statement

    13. Features of the Modified Combinatorial GMDH algorithm

    14. Example 1: Price impact on Sales

    15. Everyday Price Driver

    16. Example2: Sales Drivers Decomposition

    18. Forecasts Reconciliation

    19. Existing and New SKUs - Distribution vs SRI

    20. Clusters of users by profile We see a clear trade of between Tina and Charlotte High performing shops have more Charlottes

    21. Used for: - Classification, - Clusterization, - Stepwise forecasting of multidimensional random processes by complexing (weighted addition) of analogues (similar patterns) taken from historical data. The main parameters of algorithm are optimized by sorting of discrete number of possible variants by inductive algorithm. The main assumptions here are following: Investigated object can be described by multidimensional process; Multidimensional process is sufficiently representative, i.e. essential system variables are included into the input data sample and it contain sufficient number of observations; Part of previous behaviour of system in past can be repeated in a future.

    22. Analogues Complexing (AC) GMDH Algorithm

    23. Analogues Complexing In rigid complexing of F predictions, the output prediction pattern A0F is defined using weights ?i of analogues complexing (1) ? (2) ? where l0i Euclid distance between initial pattern and analogues; F number of predictions.

    24. Steps of sorting The general problem of AC algorithm parameters optimization can be solved in four steps of sorting, in four dimensional space: 1. Set of input variables X; 2. Number of analogues F for complexing; 3. Length of analogues k; 4. Weight coefficients ?i values for analogues complexing.

    25. Example of Forecast by AC algorithm

    27. BenchmarksNo Deal Volume Sales Rate Index

    28. Supply chain planning traditionally uses shipments out as primary demand signal Often fails to integrate recent shifts in retail POS activity and the impact of the customer level, promotion activities Results in significant disconnect between the true demand signal (retail POS) and the manufacturing plans for inventory deployment, production and product allocation Connecting supply chain planning to POS consumption offers: Better allocation of product to accounts Improved operations visibility to downstream demands and promotional activities Reduced pipeline inventory, Reduced out of stocks Supply vs. Demand & Supply Forecasting

    29. Consumption vs. Shipments

    30. Reporting software system Main focus on knowledge discovery than on input data handling. Was designed as a fully automated interface for analytic functionality to produce immediate insights in a reports using: Long-term forecasting for product launches. Scenarios simulation using marketing mix models. Media analysis. Flexible automated division of products and clusters analysis. Decision support helps to point out the most important variables for launch or to find features for the future product. The deck of slides is generated directly from the system. Robust data import and export of products clusters.

    31. Solution of modern data mining problems require consecutive application of different inductive and deductive methods to get complex solutions Several techniques should be used in the GMDH algorithms to receive unbiased and accurate results Large data allows us to test algorithm on several new data samples simultaneously Huge size of data require application of special new tools

    32. Gregory IvahnenkoPeerius, London UK

    34. MR/Insight Involvement in the NPD ProcessThe proposed approach can be applied at almost all stages This is a very top-line framework of the insight and research process that might accompany a new product launch. It could be useful to use this as a conversation starter - is this what your client does, how is it different, where do they focus resource, how do they currently set targets, how often do they meet them, what key performance measures do they currently use for NPD etc. etc.?This is a very top-line framework of the insight and research process that might accompany a new product launch. It could be useful to use this as a conversation starter - is this what your client does, how is it different, where do they focus resource, how do they currently set targets, how often do they meet them, what key performance measures do they currently use for NPD etc. etc.?

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