The more they try, the more they are likely to come back!!

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Are you trying to launch an online site for customers? Do you know that on an average, 60% of users do not return after using the site once?

In this research, we discover factors that predict whether first-time users return to MovieLens, our movie recommendation site.  A model based on these factors successfully predicts 70% of returning users (and non-returning ones).  Notably, the best single predictor of user return is the diversity of features explored in the user’s first session!  Along the way, we develop a process and a metric for activity diversity — one that can be applied to any site or context. Interested in further details?

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How do People Ask for Recommendations?

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While your TV’s remote might already have a microphone in it for voice commands, it is no replacement for a video store clerk. The current generation of devices respond to a limited set of commands, offer mostly shallow integration with deeper personalization, and may not understand complicated recommendation-seeking questions. Our research aims to develop techniques that can bring together voice recognition technologies, personalization, and advanced search features to provide more natural ways for people to discover new digital content. (more…)

87% of People Got This Question about Their Door Lock Wrong!

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You drive home and park. Your car is full of groceries and other shopping, which take many trips to bring into the house. Five minutes after you drove in, you are still making trips to the car. Is the door locked or unlocked?” What if I told you that 87% of people got this question wrong? Sensors and “smart” devices for your home may hold the promise of making life more convenient, but they may also make it harder to understand and predict things like the state of you “smart” door lock in common situations like the one above. Want to give it a try?

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