Skip to main content

Online Shopping Identity - Identifying Bad Merchants, Bad Customers

Ever came across a bad merchant operating under different alias name on eBay and CraigsList…ever came across a customer who always uses and returns product in the 30 day money back trial…let’s identify them and understand an architecture solution option for this.

In a patent disclosure 20110047040- Alias Identity And Reputation Validation Engine, the inventors Mark Carlson, Patrick Stan, Patrick Faith, Benjamin Rewis present methods and system which provides online identity and reputation information.

With the prevalence of multiple online shopping sites, it is common for merchants and customers to have different alias (aka user ids) on different sites. This means that if a merchant selling products on eBay gets a lot of negative feedback for poor quality, he just closes that alias and opens a new alias on eBay or CraigsList. Similarly a mischievous customer keeps on having multiple alias on same or different sites. It would be prudent in such online business environment to have a common reputation and identity mechanism which can link together all the alias and provide combined transaction history, feedback, rating etc.

The alias profile of a merchant usually includes information about the financial institutions used by the entity to conduct its business, the complaint history, information about the server computer used by the entity, any other aliases used by the entity, location of the entity, transaction information about the entity, and any ratings information.

“Since the reputation and validation engine captures information about an alias from various sources … even though the merchant is new to the second marketplace, the alias identity and reputation validation server computer can analyze the information gathered from the merchant at the second marketplace and match it with information received from the first marketplace and conclude that this is the same merchant.”

For the purpose of simpler instance, let us have a payment processing and analysis module communicate the collected raw transaction data to the data collection module. The data collection module can be a Apache Hadoop cluster with HDFS to store data across nodes.

The raw transaction data is transmitted to alias indexing module which uses HBase for supporting indexed and real-time queries. The module does not provide detailed information on profile or history – just indexed results on transactions per merchant or customer. For instance, if during a transaction, the history of a merchant is queried, the web portal can invoke a query from its web server cache which has been periodically refreshed or updated real time from HBase.

The alias profiling module is the one which creates linkages between various transactions and the aliases/objects that are involved in the transactions.  Although the inventors don’t mention usage of MapReduce, it is one of the algorithm approach that can be used to arrive at a single common view of information per merchant or customer.

Similarly, the information captured and processed can be used to predict entity behavior also. For instance, if a merchant has a history to delay shipments of mobile phones but has a good track record of shipping TVs, the customer can have that info handy. Similarly, if a customer has a history of buying in first week of each month, the shopping portal and specific merchants can send interesting offers during the last week of each month.

In a real world, however, the challenges of course remain with fierce competition between various portals and lack of data sharing among the portals. However, the technology solution is here and with some online market consolidation expected in next few years, we can expect better identity management in e-commerce.

©, All Rights reserved

Apache™ Hadoop™ HBase™are the trademarks of Apache Software foundation.


Popular articles

5 online tools in data visualization playground

While building up an analytics dashboard, one of the major decision points is regarding the type of charts and graphs that would provide better insight into the data. To avoid a lot of re-work later, it makes sense to try the various chart options during the requirement and design phase. It is probably a well known myth that existing tool options in any product can serve all the user requirements with just minor configuration changes. We all know and realize that code needs to be written to serve each customer’s individual needs. To that effect, here are 5 tools that could empower your technical and business teams to decide on visualization options during the requirement phase. Listed below are online tools for you to add data and use as playground. 1)      Many Eyes : Many Eyes is a data visualization experiment by IBM Research and the IBM Cognos software group. This tool provides option to upload data sets and create visualizations including Scatter Plot, Tree Ma

Data deduplication tactics with HDFS and MapReduce

As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction. Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability. From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets. Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus o

In-memory data model with Apache Gora

Open source in-memory data model and persistence for big data framework Apache Gora™ version 0.3, was released in May 2013. The 0.3 release offers significant improvements and changes to a number of modules including a number of bug fixes. However, what may be of significant interest to the DynamoDB community will be the addition of a gora-dynamodb datastore for mapping and persisting objects to Amazon's DynamoDB . Additionally the release includes various improvements to the gora-core and gora-cassandra modules as well as a new Web Services API implementation which enables users to extend Gora to any cloud storage platform of their choice. This 2-part post provides commentary on all of the above and a whole lot more, expanding to cover where Gora fits in within the NoSQL and Big Data space, the development challenges and features which have been baked into Gora 0.3 and finally what we have on the road map for the 0.4 development drive. Introducing Apache Gora Although