Building a Behavioral Profile Model

Intro

The majority of us go shopping. We purchase all kinds of things, starting from simple essentials such as meals to various entertainment venues, for example, music. While we are shopping, we’re not simply discovering stuff for using in our everyday life, also we reveal our involvement in different social institutions. Our behavior and choices on the internet create our behavioral profiles.
When we purchase an item it has some features which can differ or make the same from each other stuff. For instance, the value of product, dimensions, or kind are instances of various characteristics. Additionally those numerical or itemized arranged characteristics, also there are text characteristics which are not itemized. As an example, the text of item information or consumer testimonials is also a type of various characteristics.
Analysis of textual content along with other natural language processing procedures could be really useful for extraction of interpretation from those unstructured textual content, which generally is beneficial in duties such as behavioral profiling.
This post presents an example of the way to build a behavioral profile model with text classification. It tells how to use SciKit, effective Python-based machine learning program for creating models and analysis for implementing this model to simulated consumers and their product or service buying history. In this particular scenario, you’ll build a model that assigns to customers one of the listed music-listener profiles, such as raver, goth or metal. The task is founded on the particular products every customer buys along with the interacting textual product info.

The Scenario

Take a look at the listed scenario. You possess info allocation which contains various consumer profiles. Every profile consists of a selection of brief, natural language-based information for any product which customer bought. Listed is an example of product info for a boot.

Description: Rivet Head offers the latest fashion for the industrial, goth, and darkwave subculture, and this men’s buckle boot is no exception. Features synthetic, man-made leather upper, lace front with cross-buckle detail down the shaft, treaded sole and combat-inspired toe, and inside zipper for easy on and off. Rubber outsole. Shaft measures 13.5 inches and with about a 16-inch circumference at the leg opening. (Measurements are taken from a size 9.5.) Style: Men’s Buckle Boot.

The objective is to classify every one existing and upcoming customer into one of the behavioral profiles, according to product info. Here is demonstrated the example: the curator is using product samples for building a behavioral profile, a behavioral model, a customer profile and last of all a customer behavioral profile.

The primary step would be to consider the function of a curator and grant the system a concept of every behavioral profile. One method for this is manual seeding the system with samples of every item. These samples will assist in the definition of a behavioral profile. In terms of this argumentation we’ll categorize the users into one of the musical behavioral profiles:

  • Punk
  • Goth
  • Hip hop
  • Metal
  • Rave

Provide types of products defined as appearing punk like information of punk albums and music groups, for instance, “Never Mind the Bollocks” by the Sex Pistols. Other items should consist of things regarding hairstyle or clothes.

Software Setup

Every needed information and source code could be obtained from the bpro project on JazzHub. When you’ll get the data to make sure you have installed Python, Skikit Learn and all the dependencies.
Once you unpack tar you will see two YAML files which contain profile information. The product descriptions are artificially created by using a body of docs. Periodicity of word occasions in product descriptions has recognized the process of creation.
Two data files are provided for analysis:

  • customers.yaml — Contains a list of consumers, for every customer included a list of products descriptions and also correct behavioral profile. The correct behavioral profile is that which you know it is truly right. For instance when you are reviewing data of user goth to verify that these buys show that the customer is definitely a goth user.
  • behavioral_profiles.yaml— Contains a list of the profiles (punk, goth, etc.), as well as example list of products descriptions which explain that profile.

Building a behavioral profile model

You should begin with creating a term-count-based depiction of the body by using SciKit’s CountVectorizer. The body object is a basic listing of strings containing product descriptions.
The next step is to tokenize product descriptions into personal words and create a phrase dictionary. Every phrase located by the analyzer throughout the procedure of setting is assigned a unique integer catalog that tells a column in the output matrix.

You can get an output of some items to check which was tokenized. Just use command print vectorizer.get_feature_names()[200:210].
Keep in mind that existing vectorizer is without “stemmed” words. Stemming is a procedure of receiving a basic source or origin form for inflected or derivative words. As an illustration, big is a basis stem for the word bigger. SciKit is unable to manage more engaged tokenization, like stemming, lemmatizing and compound splitting, however, you are able to use specialized tokenizers, for example from Natural Language Toolkit library.

The procedures of tokenization like stemming make it easier to decrease the number of needed training samples, due to the fact numerous forms of a word don’t demand statistical depiction. You may utilize additional tips to decrease training demands, like applying a dictionary of types. As an example: in case you have a selection of goth musical group names, you are able to build basic word token, like goth_band, and include it to the description before creating functions. Considering this even if you’ll meet a band initially in a description, the model manages in the process where it manages alternative groups which types it knows.

In the computer learning, monitored specification troubles like this are posed by initial definition a set of features along with a matching target. Then the selected algorithm tries to locate the model with the best suitability to information, it reduces faults against an identified set of data. Consequently, the next step is to create the characteristic and target label vectors. It is usually wise to randomize the monitoring just in case verification procedure doesn’t do that.

At this point, you are in a position to select classifier and educate your behavioral profile model. Before this, it will be wise to examine the model to make sure it works.
Once you’ve built and tested the model, you will be able to test on many user profiles. You may use the MapReduce framework and transfer trained profiles to work nodes. Every node then receives a set of client profiles with their buying history and applies the model. Next, when the model is applied, your clients are placed on a behavioral profile. You are able to use profile assignments for many purposes. For instance, you can use it for targeting promotions or use the recommendation system for your customers.


Watson Sensory Input Overview

Watson’s pros and cons

Plenty of features that allowed Watson successfully participate in its Jeopardy! performance also help it become extremely suitable for typical jobs that require massive segments of natural language information. Lots of aspects make understanding and discourse concerning natural language problematic. Due to Watson relies on plenty of these points, it gives completely new process to the style computer systems may add benefit to our life. This article explains a method for improving Watson with the ability to automatically detect relevant non-textual information. You may consider these upgrades as providing to Watson “eyes and ears”.

Watson is notably effective in:

  • Proper performing on unstructured content, especially text – However multiple systems allow for the computers to work with natural language data, majority of systems finish with what volumes to little more than the capability to index different phrases. Watson can take synonyms, puns, sarcasm and much more forms of speech. Watson is able to absorb and efficiently work on content starting from technical documentation to blogs and wiki articles.
  • Effective operating with big amounts of reference material – one way computers have successfully resolved difficulties is to use their performance for assistance on dealing with big volumes of data. Exploring a database with millions of records will happen in a flash. For medical doctors it is physically extremely difficult to read and keep in mind all the relevant data being generated daily. The system should have ways to understand which data belongs to each other among billions of records. Watson provides such technology which is helpful for this aims.
  • Learning potential – world is changing. To keep relevancy of developing troubles and increasing information bases, solution should be dynamically fit and study. Watson’s skills to learn and modify via basic user communication retains technology relevant and constantly enhancing.
  • Human interaction – During the history of computer machines users have had to accommodate to interact with the system on its conditions. Though that solution is good for individuals that are ready and want to learn different idiosyncrasies for every new solution. With the development of Watson human-computer interaction is shifting to the level where the system is able to effectively and, in common, interact chatting with human users on their conditions. This human way of conversing is now a normal practice. The ability to automatically structure and require follow-up questions in natural language is an effective technique for user interaction.

Even after its Jeopardy! success Watson received lots of improvements. The size and power of the footprint have been significantly decreased together its functionality are reguraly increasing. But while Watson is optimized to work with natural language, content, context, and interaction, it is not able to deal with sensory input. Watson has no sensory interface to function as eyes and ears. It will just reply on a context which has been defined in textual form.

The “meaningful ask”

To communicate with Watson sensory information should be translated into an understandable form such as text. For instance, suitable picture can be a medical X-ray image. A human radiologist will understand this image by using a contextual explanation.

The technology to have the computer automatically create this explanation is in truly initial phases. But for lots of other types of sensory handle – such as sound recognition that a sound from a specific breed of dolphin or that heart beat waveform tells about particular form of tachycardia. We can automatically transform obtainable data into a text description.

Dr. Alex Philp of GCS researching represents this translation procedure as transforming sensory information into a meaningful ask. Due to the reason Watson can’t recognize sound, it is not able to listen to it directly and explain to you the meaning of the sound. But in case when sound is processed and converted into descriptive phrase included in query or as a context to question, Watson could reply properly. This process of translation generates the meaningful ask.

Human-in-the-loop

While computer systems are consistently getting more clever and smart, people perform an essential role through application developing and deployment. Contrary to lots of back-office or machine-to-machine programs, the majority of Watson-based products are tailored for human interaction.

In the process of creation, professionals work with Watson to determine which sets of information needs including in its data body so to tweak the direction in which information is applied. Watson continue to rely on a long-term training stage where human specialists regularly communicate with it by improving desired reasoning routes, de-emphasizing the unwanted ones, and determining resources which need to be included to body.

When a method is installed for usage, humans interact with it via interface received from application. An effective factor of Watson is its potential to have an open, constant dialog with user. Watson can remember where it stays in dialog and constantly monitor the full set of conversation relevant context. This behaviour allows to prevent the need to continuously re-enter similar data, and it enhances the precision of answers. In the case explained in this article the obtainable sensory data gets to be component of interaction. For example, if a physician interacts with Watson for a certain patient, the sensory system automatically involves into the context, related materials to patient history and present status. Examples of medical telemetry could include specifically sensed information like heart rate, blood pressure, temperature, blood-oxygen saturation, brain wave patterns etc. Additionally the real-time analytics of the stream-processing method could produce artificial telemetry by detecting conceivably faintly discernible patterns or correlations.

Take into account advantages of improving Watson therefore it could get straight sensory input and data from electronic medical systems. This feature will eradicate require for the medical professional to specifically explain lots elements of the patient status so to concentrate on other activities which couldn’t be automated with present technologies. Preferably, a doctor could ask the question: “What is the reason of the slow respiration for the patient in bed 32?” and receive all the needed contextual data. Watson will reply with a selection of potential reasons and levels of accuracy.

In the far future, computers may have advantageous intelligence than people. Someone could debate that from that point, they are getting more humans than we are. However, the best strategy is by using computers for that they are ideal at, like lurking throughout large volumes of data to provide objective results, and to keep opinion regarding those results in hands of people. This method is practical for 2 factors: computers are not enough precise to invariably believe them, and our life experience, mixed of the route our brain is wired, deliver another level of understanding and judgement. Thinking of humans and machines is differ but complementary. Collectively they are an effective mixture. Including sensory input to Watson improves this combination with delivering extra data to shared context.

Noticing and gaining knowledge from results

Contrary to Jeopardy! game where every question and reply were self-contained, majority of solutions created for Watson nowadays are aimed at a ensuing activity, for example advice of medical care or an item to buy. In certain circumstances the inclusion of sensory input to Watson may allow to instantly monitor and study from results of its suggestions.


User Request Web Content Prognostication

Discover ways to examine web server log files to learn ways of users website browsing and forecast next browsed content. This article explains applying extensible Markov model to cluster web pages on a website and predict the place user will move next. The algorithm utilizes InfoSphere® Streams and R for regular issue prognostications based on model.

Preamble

Webserver log files are used to examine users surfing behaviour. As an illustration, in “Predicting Web Users’ Next Access Based on Log Data”, Rituparna Sen and Mark Hansen have utilized combination of first-order Markov models to examine clusters of pages on a website. They applied these models for prognostication which webpage user supposed to visit next. They suggested implementing this information to pre-fetch a resource before a real request by user. This article will explain how to use IBM InfoSphere Streams, combined with R to run an identical analysis of webserver logs.
This solution is implementing extensible Markov models (EMMs), initially released in 2004 by Margaret Dunham, Yu Meng, and Jie Huang, to mix a stream clustering algorithm with a Markov chain. A Markov chain is a mathematical system that reviews transformations from one state to other, in which the following state is relying only on present and not the sequence of proceedings that came before.
The states of Markov chain are aggregation specified by stream clustering algorithm. The EMM can transform eventually by including new states since they are discovered and also damping or trimming current states with time. Consequently, the model is able to make adjustments eventually. This opportunity is particularly crucial in systems with dynamic usage style that changes over the time. As an example, website will probably display dynamic usage pattern, as well as improvements in structure, in some time.

Advantages of integration

The majority of machine learning models designed for forecasting are performed offline on big amounts of training info. Right after the model are properly trained, prediction could be done right away. This technique is suitable for numerous sorts of issues, however if the patterns for prediction are changing regularly, this method could create models that drop behind the system they are attempting to forecast. Since EMM could be educated dynamically, they are effective for modelling systems like network traffic, auto traffic, or another system in which clustering patterns can transform eventually. Web server traffic is one of those sphere. Server logs deliver an infinite source of streaming information to educate the model when the system is already performing forecasting.

Prognosticating content requests from web server logs

Internet servers are keeping logs of resource queries. Every log entry consists IP address of user, timestamp for request, and the destination for requested data. All this information characterize user and requests to website.

Summary

This article shows how to forecast users actions on a website to predict content requests using webserver log files. The modelling and prognosticating are completed by applying EMM. The solution represented here is a testament to concept. Upcoming work is essential for developing a genuine solution. Next actions involve enhancing overall performance by clustering sets of webpages, incremental studying, and using InfoSphere Streams to carry several cases of R.


15 Practices of SEO Copywriting for 2014

I’ll bring disappointment to businessmen and marketeers: SEO Copywriting practices won’t give you guaranteed SERPs. Easy and quick way to increase your sales and conversion doesn’t exist. Even if you’ll find tips and genuine tricks which are placed periodically you won’t get any idea what exactly will make your ranks increase in near future.

However, market best methods remain really valuable to use. These are trustworthy copywriting and SEO strategies – efficient techniques that in the long run have shown efficient in ranking, traffic raise, sales, click-through rates. They might furthermore strengthen trustworthiness and reputation.

We’ve posted list of 15 best techniques for 2014 year. All are following SEO and content guidelines. Use them to increase possibility of high ranking.

1. Write Initially

2014 Search engine optimization copywriting is focused on quality – nicely-written optimized material that attracts people. It stands to reason to write initially and then fix for search engines. Simply sort keywords after writing and this method will save you considerable time.

2. Attract

Attracting your audience as a article writer implies interacting with them. What will you do to engage your readers? You should begin with catchy title. When it is done you should keep your audience attracted. You can do this with creating connection on emotional level.

3. Apprise

Information is a gold. Once you’re promoting something people don’t want you to tell them it’s best product to increase sales. They want to know what benefits they will get from your service or product.

4. Relevance

Relevance belongs to aspects which influence on quality, so stay tuned. You shouldn’t bother if an article is irrelevant to your company. If your website, for example, provides domain name registration filling your content with information about internet marketing will only bring you to loss of audience.

5. More Than 300 Words

Though search engines have never mentioned about definite post length webmasters made conclusion that longest articles with 300 or more words length usually engage better. Longer articles are more attractive, therefore it is wise.

6. Remember Precepts

Definitely this tip is one of the best. You may seeking a way to influence on search engines, but keep in mind what copywriting is all about: marketing and selling. Your aim is to get conversion. And you’ll get it if you’ll remember commandments.

 7. Create Skimmable Articles

To reach best benefit your article must be quickly read through. To explain, you should involve headings, subheads, bullet points and enough free space while writing content optimized for search engines. Outline main points with bold and italics.

8. Study Keyword Research

Keywords remain a nutshell of search, therefore it is important to understand how to find those which are bringing traffic. After Google swapped their old tool with new Planner, multiple SEO writers discovered it became harder to do keyword research. As an alternative you can use Moz Guide and SEJ Guide.

9. Keyword per Page

Proper search engine optimized article considers one topic per page. Also good SEO is targeting one main keyword per page, usually phrase with big number of searches and less competition. Other used must be associated to main or long tail keywords.

10. Optimize Body

Apply your main keyword 2-5 time according to length of article all over body copy. “Keyword density” have stopped being relevant to SEO and copywriting, but “over-optimization” still actual. So you should take care of number of times your keywords appear in article.

11. Start with a Question

Rhetorical questioning is a good method to start your article, it calls curiosity and attract readers. That might be your first sentence. By the way Google’s Hummingbird appears to like keyword-rich questions.

12. Hyperlink Keywords

While linking to your article from other pages or linking out from your content it can be useful to create link with text keyword. However, while performing this it is crucial not to use same phrase constantly, particularly within one page.

13. Read Your Article Aloud

Reading you article aloud could lead you to another point of view. Lots of good content writers do the same. If you think this is strange or don’t like your voice try text-to-speech software. Or ask your relatives to do this.

14. Call to Action

No matter blog or copywriting most likely you’d want your visitors to perform actions on your website, so ask them to do this. Call-to-action like “click here to register now” is working great even nowadays, also properly planned CTA that include keywords can raise conversion rates.

15. Go Social

The planet is social. If you’re not benefiting from social media networks you’re loosing a lot. Professional copywriting is insufficient. Motivate your audience to share your articles via social website with social buttons on your website.