Cookie – Tracking user behavior & recommendation

Cookie is a short code to tracking user behavior when surfing in the internet, reading news and article, watching video and podcast and audio program. From cookie collected data, we can understand who, which, where and when content clicked and dwelling time. When you google, google cookie will assign a unique identity (UUID) to you, and trace you, similarly when you Baidu, Bing. But the UUID is different in Google, Baidu, Bing because UUID is not cross browser. But when you login different browsers using same Email account, these UUIDs can be linked and identified as a single user.

Different cookie is used to track different user behavior. For example, cookie tracking user surfing news is different from tracking user watching TV program, or listening radio channel. Third-party cookie service is often used in media company to support news recommendation, audio program recommendation, video program recommendation. There are many DSP (data side platform), DMP (data management platform), and SSP (supply side platform) to provide technology services, e.g. cxense, lotame, ……

Media company often requires customized recommendation system. Third-party service provides cookie and widget toolkit to satisfy customized requirement. For news recommendation, through the widget setting, the customer can configure news category, keyword, name entity, term weighting, period, blacklist & whitelist. These functions can satisfy basic business requirements on news recommendation. This is traditional information retrieval application in news, and cannot do personalized news recommendation, which is widely applied in Google, Facebook or Microsoft Bing search. In-house data science team can exploit internal audience data to understand user interests, build machine learning model to do personalized recommendation. In practice, most companies have no such capability.

For audio / podcast and video program recommendation, most of time, it is still treated as a text information retrieval problem. These program have meta text description such as caption, short description of program, editors or reporter names, program director and actor names. Using these available meta data, recommendation can fulfill most business requirements. Audio and video/image processing and content understanding are not widely used. It is not only because of less manpower capability but also because of hungry computing resources to processing audio and image. In terms of ROI (return on investment), they may not be a good investment.

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