Harry Chen Thinks Aloud

Netflix is holding a contest to improve its movie recommendation engine. Anyone who can improve the service’s recommendation function by 10% will win a $1 million prize. While this may be a great opportunity for academia research, especially for user modeling, machine learning and recommendation systems research, but I doubt that this contest will help Netflix to create any new and revolutionary recommendation systems. The main problem is in the user rating data.

Netflix may have the largest user rating data, but it alone is insufficient for creating an effective movie recommendation system. In order to create a such system, many other user attributes must be considered. These include, but not limited to, user profiles, the usage and behavior pattern of a Netflix account.

Use Sterotypes to Predict Favorite Movies

User profiles can be useful predictors for future favorite movies. While people may dislike the idea of using profiles or stereotypes to categorize movie watchers, but I believe it can be more accurate in determining user favorites than pure statistical data. Here is an example that shows why user profiles are important.

My wife and I are both Chinese and with a strong Hong-Kong culture background. If you study our Netflix queue and our past movie ratings, you will find that not only we watch a lot of English-language movies, but also many Chinese-language movies, especially Hong Kong movies. Many non-Chinese Netflix users also watch these movies. However, our respective opinions about those movies are quite different. While some differences are rooted in personal preferences, but some others are rooted in divergent culture background. Movies that are funny to a Hong Kong native may not be so funny to other groups of people, and vice versa.

People Share Netflix Accounts

Although I have no evidence to prove my theory, but I believe Netflix’s user rating data assumes that user ratings came from the same Netflix account were made by a single person. I believe that’s not true for most of the Netflix accounts. Many families only subscribe to a single Netflix account. In my family, my wife and I share a single account. We both actively manage this account and rate movies. It’s inappropriate to consider our combined ratings as the ratings of a single person. Just because I like action movies and my wife likes comedies, one can’t conclude with full confidence that we as a single Netflix account user like both action movies and comedies.

People Rent Movies for Others to Enjoy

Another problem associated with a single account usage is that a user who rents all the movies may not be the same person who actually watches them. For example, parents may rent movies on the behaves of their children. While they rent movies, but they may not watch all of them. In fact, parents may not even enjoy watching them at all. If a parent rents a half dozen of cartoons and 1-2 action movies a month, should the recommendation engine automatically assume that cartoons are the user’s favorite? What if the user continues to rent cartoons but at the same time rate those movies with very low scores (1 out of 5 stars), is cartoon still the user’s favorite movie?

Missing Features: Better Browsing and Social Networks

I believe that with user rating data alone is insufficient to help Netflix to build an intelligent recommendation engine. Lacking detail knowledge about individual user profiles and their account usage pattern are key weaknesses in this approach.

I’ve identified few other types of user information — user profiles, usage behavior and patterns of a Netflix account — that may help to improve the accuracy of machine recommendations. However, there is one problem. The collection and use of this user information may cause great concerns for privacy issues. It’s unlikely that I’ll be willing to share my detail personal information with Netflix and its intelligent recommendation engine.

Instead of focusing on building a super-intelligent recommendation system, I think Netflix should focused on improving other simpler but essential features, such as movie library browsing and social network interactions. As a Netflix user since 2002, I recall myself rarely paying any attention to those machine recommended movies. I usually pick my movies by browsing through different movie categories. I think many other Netflix users share my experience.

If browsing is what users use most of the time, Netflix should spend more efforts to improve and enhance its functionality. If Netflix can do something like movietally, that would be great!

Related links:

• Netflix to release user rating data (EBB)
• MovieTally: Netflix’s Missing Features (TechCrunch)