The Collective versus the Individual Consumption Pattern

Both the terms collaborative filtering and personalization are integral to the attention economy that is apparent on the web. As attention becomes a scarce resource, an increasingly higher number of web-based businesses or e-commerce in general, in addition to vast array of virtual communities that seek to attract your attention, seek to do so under the veil of the illusory attention. This process, in turn, gives the reader or the web user the impression that he is being targeted personally by the the message that was brought to his attention. Nevertheless, although similar in their end, these terms differ in their means.

The term personalization, in some circumstances interchangeable with customization, implies that the person being targeted by the message is receptive to it because it was able to attract his attention based on a series of data that are collected and that reveal the individual's consumption habits on the website. According to the website www.boxesandarrows.com, "Web personalization is a strategy, a marketing tool, and an art. Personalization requires
implicitly or explicitly collecting visitor information and leveraging that knowledge in your content delivery framework to manipulate what information you present to your users and how you present it." One example of personalization is the algorythm- model for Google Ads, which scans an individual's e-mail in order to detect key words that would contribute to changing the ads bordering the email according to the e-mail's content. Similarly, Wikipedia states as part of its definition of personalization that in that case, "web pages are personalized based on the interests of an individual. Personalization implies that the changes are based on implicit data, such as items purchased or pages viewed." In this context, it is not entirely sure
if Google Ads fits this criteria, since although it is based on implicit data, it is unknown as to what implicit data truly is. It is unsure for example, on what criteria this implicit, or even explicit data is based; one does not usually how the data collected, since some
data is collected on key scans and other on buying habits of the consumer.

In contrast with personalization, collaborative filtering might be a better suited marketing or attention-drawing tool for the web user. Once again, wikipedia offers the following definition of the term, which consists in looking "for users who share the same rating patterns with the active user (the user whom the prediction is for)" and in using "the ratings from those like-minded users found in step 1 to calculate a prediction for the active user." The collaborative filtering seems to be more a better suited tool because it takes into account that attention is not attributed individually, but rather to fragmented consumer groups, but whose tastes may overlap. As such, a consumer wanting to buy a CD on Amazon's website, may find the different sorts of music that people buying the same CD listen to. For example, if a consumer types the album title "Peines De Maures / Arc-En-Ciel Pour Daltoniens" from the French rap group La Caution, it will show a section called " Customers who bought this item also bought" and list a variety of album purchases from Gotan Project's La Revancha del Tango to the soundtrack of the movie Ocean's 11. Consequently, collaborative filtering fulfills a more adequate role in terms of web 2.0 criteria and the dynamics of virtual networks, for it allows individuals to collaborate and reach a subjective consensus on a series of data that allow individuals to share information. This not only allows them to contribute to a source of information but also to gain from this mutual exchange of information that is enabled by bringing one individual's attention to the sort of products that others have bought and that he or she might be interested in.


Although both processes are marketing tools, collaborative filtering follows rules of common consumption patterns and allows for consumers to expand the horizons of their cultural consumption tastes. In contrast, personalization only suggests certain products based on the individual's consumption pattern, thus limiting his active role in seeking out what others who who bought the same product might be buying. The collaborative system is a system based on
consumer reciprocity, which allows for personalization of tastes by expanding the individual's original consumption tastes via a system of information sharing.


-Nik Bhowmick