Blend of Social & On Site activity to create personalized Recommendation for eCommerce

Read some interesting articles on how eCommerce companies are defining future of technology


http://pro.gigaom.com/blog/going-social-recommendations-engines-need-to-factor-in-consumer-reviews/

https://www.sailthru.com/smartdata

Sailthru powers Fab Ebay planning to acquire Hunch

Both use different ways to increase conversion.

Hunch basically considers social reviews and fans & other social activities built around a customer
Sailthru uses propensity score modelling based on demographic info gathered around each visitor coming to eCommerce site and gives personalized recommendations.


How about an engine which provided best of both the world.

A system which integrates social media reviews/ social actions and internally creates & collects demographic info of each visitor on site and then sells its API to all eCommerce major in India.

Seeing the price these companies have been sold and cash eCommerce companies Indian companies have this can/ and probably shall be future of eCom in India


Article -2011

Even though eBay has its own recommendations system based on searches and popular items, eBay grabbed Hunch’s recommendations engine. The acquisition illustrates the trend of adding social media to the data-powered recommendations credited with increasing Amazon’s sales by 25 percent. But get ready, because the next stage of commerce recommendations will further mix in human-powered consumer reviews.

Pioneered by Amazon, the first wave of e-commerce recommendations engines were based primarily on collaborative filtering. That is, they compared a lot of data: “Customers who bought product x also liked product y.” As with most big data analysis, recommendations are not only about pattern matching but also about which patterns best predict future purchases. And like all pattern matching, the company with the most data usually wins. Social media, via open APIs from Facebook and Twitter, bring new data for analysis and lower the cost of entry for recommendations engines.

Game-changing social data from APIs

Hunch creates a “taste graph” of a user’s interests based on analyzing available social media data from Facebook, Twitter and others that is fine-tuned by user responses to a Q&A process on the Hunch site. That’s why it could deliver useful results without suffering from a “cold start,” or lack of enough data to be predictive.

Effective e-commerce and media recommendations will mix in big-data analysis of different information sources including product factors, observed purchase behavior, and social and interest graph data from APIs. They will create algorithm-driven engines from that data and present it alongside customer reviews that themselves can be filtered and analyzed by adding structure through categories, comparisons and ratings schemes the way Amazon and Best Buy do.

Another recommendations pioneer, Pandora creates personalized radio stations based on factor-based analysis somewhat like Amazon. Pandora programs based on mapping and matching song and artist characteristics, along with a user’s likes or dislikes. Its effectiveness makes it the leader in online radio, with 40 million active users and $75 million in third-quarter revenues. Lately, Pandora has been scrambling to build out social features to enhance its website and apps and get more inputs.

A startup with an MIT Media Lab background, The Echo Nest has begun licensing its music recommendations engine to online music programmers like KCRW and Clear Channel’s IHeartRadio. The Echo Nest automates its song-characteristic analysis by running each track through audio analysis. It also scans online blogs and music pubs for information that informs its audio analysis. This makes its approach more scalable — and cheaper — than Pandora’s engine.

A blended approach in the future

The established engine technologies still work, and social media data lets new players build them effectively. Last year, Amazon started experimenting with available social data. When connected via Facebook, Amazon remembers a user’s friends’ birthdays, suggests items popular among friends and makes recommendations based on favorites the user has listed explicitly on his Facebook profile. But so far, all of those results are on a separate, “beta” page of recommendations that Amazon links to from its personal recommendations page. They’re not mingled with Amazon’s traditional recommendations and user reviews on product pages — yet. Likewise, as it learns what social data is most predictive, I expect Amazon will incorporate the social signals it gathers from Facebook connections into its recommendations-ranking algorithms, the way Microsoft uses Twitter and Facebook in search.

Successful recommendations will take this approach of blending social and data. At GigaOM’s RoadMap conference, Wal-Mart sketched how its social commerce strategy, driven by its Kosmix acquisition, would focus on search, recommendations and local, in-store context rather than stores on Facebook. Wal-Mart is one retailer with serious data smarts. This is just one example of how, for commerce, it’s wisest to think of social media as data sources rather than shopping hubs.

Dejavu

This is strange that I am starting to write after years with this post...

Well past few weeks for me have been been real difficult, kind of strange for me. feeling uneasy restless feared for no known reason.
Hard to concentrate on professional work assignments when all was going really well earlier.




I get irritated with small failures like not able to make changes in excel or get data dump from GA. In spite the task being no more than of two days, al-most two weeks have not been able to complete it. Wasting my time watching silly Youtube videos or documentaries.

Today suddenly i realized similar thing happened with me long back in 2001 same time of year. Was preparing for JEE, all was going well, cleared screening exam got decent rank too. Coaching classes my solution in Maths and Physics were always appreciated. And suddenly i find myself in tricky situation not being able to concentrate on studies in the last, final but most important lap for JEE. used to play age of empires hiding late night instead of solving thermodynamics or applying calculus to coordinate geometry.



Then i thought deeper, reminded that that year was also Maha Kumbh...
Can all this be attributed to stars, astrology?


I failed in final exam of JEE, in spite of trying all the auspecious things like visiting maha khubh taking holy bath, attending some satsang of guruji by traveling to delhi from kanpur.

Not able to predict, project the future from now for me, where am i headed to failing professionally? or in personal relationship?

After all I am married man now and supposed to be more responsible.

Scared, skeptical of the times to come in my life ahead...

Khuda Kanpur Khuda Sahare

While traveling i came across this funny quote

Khuda Kanpur Khuda Sahare

To understand a gravity of the situation please see a few pictures

The way local municipal corporation has been digging up the roads with no planing. Last month few of other main roads like Chunniganj and Navin Market were dig with no proper alternate way. Now many other roads in outskirt areas and near Company Bagh are dug.

There is no clear timeline, for the completion of work.
Worst part is with now election being announced and mode of conduct all work has been put on hold.
So for next more than 1 quarter all roads will continue to be in the current pathetic state.

being a common man sometimes hurt so bad..You see your tax money being wasted in front of your eyes and you are helpless seeing your hard earned money going down drain while some stupid politician or bureaucrat is sleeping ducks and himself blaming poor policy, planing and execution of infrastructure in this country.

Hope some bill or something may be its Lokpal or Kokepal or Thokpal whatever sorts out this menace.