Social TV has emerged a popular concept over the last year, as a means by which by members of a social network, or a community, share actions, comments and ratings on their TV screens or on companion devices and apps, thereby enriching the TV experience and the context provided by traditional synopses and cast listings and increasing user engagement. Social TV is also seen as more human, whereas algorithmic recommender systems are sometimes viewed as cold expert systems, whose relevance and effectiveness is therefore debatable: “Why is this thing suggesting me to watch Gangs of New York? I hate Di Caprio”. Although Social TV added value is real, we continue to believe in automated recommender systems very strongly. Here are the reasons:
- Recommender systems can be seen as cold, because they are objective. Their recommendations are the result of thorough analysis of users’ past activity, of their explicit and implicit ratings (e.g. abort a movie stream 10 minutes into it = bad). They are seen as cold, because the key element to improve their acceptance is often missing in the user interface: an explanation. You might not like Di Caprio, however you might be a fan of movies happening in New York therefore Gangs of New York make sense. You may or may not like the explanation, but the system becomes instantly more credible, more understandable and therefore more human.
- Social TV alone won’t solve the long tail issue. Physical and digital commerce requires to be able to move product in the medium and long tail profitably, not only the most popular assets. In fact, the most profitable products may not be the most popular. Read again “Why The Future is selling Less of More”. While Social TV tends to focus on most popular hits, algorithms know how to mix items from the entire catalog and update listings in real-time based on how people react to them.
- Social TV alone won’t solve the serendipity issue. Entertainment works only if it carries some surprise effect at some point. Algorithms know how to handle this as well, while social comments and ratings keep focusing on most popular hits, mostly known to a large section of the population or the network.
- Social TV alone won’t solve the personal taste issue. Being friends-in-life does not mean being friends-in-taste. Algorithms know objectively how to generate suggestions based on user activity tracking and can effectively separate the noise coming from social networks from valuable recommendations.
The solution certainly lies into a combination of both approaches and the technologies that support them, integration into and import from social network streams and recommendation algorithms. Think of it, wouldn’t it be cool to discover who within my social network statistically shares the same taste as I do, so that I can follow his future comments, checkins and recommendations?