Unique Insights

  • If two features are associated, it means they are interlinked and you can replace one by the other; Association relation is bi-directional.

    Having a strong association means that any time an event occurs with one feature it will most likely occur with the other.

    It also means that associated features happen together irrespective if they occur quite often or rarely. They tend to always happen together.

  • If one feature is an influencer to another, it means they are linked in one direction, from influencer to target.

    Having a strong influencer relation means that any time an event occurs with an influencer it will most likely occur with the target (being influenced).

    It also means that influence is always happening, irrespective if an event happens often or rarely.

  • If one feature is a cause to another it means that the effect with high certainty will occur whenever the cause occurs.

    If one feature is not a cause to another, it means that the effect with high certainty will not occur.

    Having a strong causal tendency relation means that any time a cause event occurs it will most likely lead the effect to occur.

    It also means that cause is always working and able to cause (or not cause) in all situations.

  • ANOMALY :

    If one features is an anomaly to another, it means that whenever the anomaly feature (which should be rare) occurs the target must occur.

    Having a strong anomaly relation means that any time a rare event occurs it will most likely affect the target to occur.

    POLYMALY

    If one features is an polymaly to another, it means that whenever the polymaly feature (which should be very common) occurs the target must occur.

    Having a strong Polymaly relation means that any time a very common event occurs it will most likely affect the target to occur.

  • To make a prediction, you need to run a series of algorithms to ensure a correct forecast is made.

    With GoFlek, all predictions are traceable and you can peek into how the prediction score was calculated.

    Having a high Prediction score  means that any time the input variables occur, they will most likely lead the target to occur.

    Unique algorithms, based on a series of multiple probabilistic measures like: Joint, Conditional, Naive Bayes, Look-A-Like, Most Frequent, Latent Cosine, Jaccard, Lift.

  • Allows to run both Item based and profile-based recommendation using Probabilistic formulas.

    With GoFlek, all recommendations are traceable and you can peek into how recommendation score was calculated.

    Having a high Recommendation score means that any time the input variables occur (which could be items or profile or both) they will most likely lead the target to occur (be recommended).

    Unique Algorithms based on a series of multiple probabilistic measures like: Joint, Conditional, Naive Bayes, Look-A-Like, Most Frequent, Latent Cosine, Jaccard, Lift.

What makes GoFlek’s calculations different from other methods?

  • Today there are two methods to measure association:

    • Correlation measure, which is based on associating two numerical variables based on their statistical similarity – they either increase or decrease together or one increases while the other decreases

    • Probabilistic, which is based on using Joint Probability as an association measure

    Why is GoFlek’s measure a good measure of association?

    • We know that Correlation only works with numerical variables – what if one variable is numeric and other is categorical? What if both are categorical?

    • We also know that Joint Probability is a weak measure of association, which is clear when we know for example that the two features have low joint probability yet they both occur together all the time (strongly associated).

  • Today there are two methods to measure influencer:

    • Regression measure, which is based on finding a formula that fits two numerical variables or more – where high coefficient indicates a variable has strong influence.

    • Probabilistic, which is based on using Conditional Probability as an influence measure.

    Why is GoFlek’s measure a good measure of influencer?

    • We know that Regresssion only works with numerical variables – what if one variable is numerical and other is categorical? What if both are categorical?

    • We know that conditional probability is a weak measure of propensity to influence, which is clear when we know for example   that the target has a high probability and influence (condition) has low probability yet the Pr(target/influence) ~= 1.0.

  • Today there is a common method to measure causality:

    • Casual Baysian Network that represents a Probabilistic Graph (DAG) with conditional probabilities with specific assumptions on independence of variables and overall probability distribution.

    Why is GoFlek’s measure a good measure of causality?

    Finding data that can be represented into a DAG and then measuring causality is prone to many unrealistic assumptions and to human interpretation; also, it is based on independence of variables which puts a heavy and unnecessary condition on the relations in the DAG.

    In view of the above limitations, GoFlek’s measure does not attempt to fit the data into a DAG and does not make any assumptions on independence between variables. On top, it measures the cause in both directions (0 to 1 and 0 to -1) because causality can have both positive and negative effects. Moreover, GoFlek’s causality measure is context sensitive – meaning that it can be measured in different contexts (whether other variables are taken into consideration).

  • ANOMALY :

    Today there is a common method to find anomalies:

    Search of rare combination of events that seldom occur (Probabilistically or Statistically rare).

     With GoFlek, Anomaly takes a more “realistic meaning” with an added condition. Having a rare event is not the only condition for an anomaly; the additional condition is that this rare event must lead to the occurrence of the target event we are measuring. For example, if a person entering the shop has blue eyes, green hair and dark skin, it is a rare even – this event does not mean anything unless it is related to the fact that this person made a purchase. Furthermore this rare event must be recurring in the sense that a person with this profile, whenever he enters the store, makes a purchase. 


    POLYMALY

    Today, there is NO concept of Polymaly in literature; this is a innovation of GoFlek.

    Measuring Polymaly is quite important.

    With GoFlek, Polymaly allows to detect a very important pattern of common events that lead to the occurrence of the target event we are measuring. For example, if a person entering a website reads a specific article in the blog section – if this event occurs it does not mean anything unless it is related to the fact the same person always fills the sales request form. For this event to become a Polymaly, it must be recurring quite often (meaning that many people read this specific blog post) and whenever they read it, they convert. 

  • Today there are common methods to make predictions:

    • Classification: Using Decision Trees

    • Regression: Estimating continuous numerical values, such as future stock prices or house values.

    • Time Series: Analyzing data sequences over time to identify trends, such as monthly revenue or seasonal patient admission rates.

    Making a prediction with GoFlek is different in the sense that it runs a series of Probabilistic algorithms to make sure that a correct prediction is made – search is exhaustive and recursive with all combinations used until a match is made. Moreover the prediction is traceable and users can peek into how the score was obtained.

  • Today there is a common method to make recommendation:

    • Collaborative Filtering which recommends items based on the behavior of similar users (e.g., "users who bought this also bought that")

    • Content-Based Filtering: Recommends items by comparing the item's profiles (e.g., genre, director, description) to the user's past consumption history and preferences.

    • Hybrid: Takes both techniques and meshes them together.

    To make a recommendation with GoFlek users are able to do both Item-based recommendation, Profile-based or both using ONE single algorithm and using Probability as a base. Our Recommendation is traceable and users can peek into how the score was obtained. Also, GoFlek Recommendation is fast because it is based on simple probabilistic algorithms compared to Collaborative filtering which is computationally expesive (because it is based on complex Matrix operations).