Recommender systems take inputs from user history, use an internal ranking algorithm to generate results and possibly optimize this ranking based on feedback. However, often the recommender system is unaware of the actual intent of the user and simply provides recommendations dynamically without properly understanding the thought process of the user. An intelligent recommender system is not only useful for the user but also for businesses which want to learn the tendencies of their users. Finding out tendencies or intents of a user is a difficult problem to solve. Keeping this in mind, we sought out to create an intelligent system which will keep track of the user's activity on a web application as well as determine the intent of the user in each session. We devised a way to encode the user's activity through the sessions. Then, we have represented the information seen by the user in a high dimensional format which is reduced to lower dimensions using tensor factorization techniques. The aspect of intent awareness or intent scoring is dealt with at this stage. Finally, combining the user activity data with the contextual information gives the recommendation score. The final recommendations are then ranked using some filtering and collaborative recommendation techniques to show the top-k recommendations to the user. A provision for feedback is also envisioned in the current system which informs the model to update the various weights involved in the recommender system architecture. Our overall model aims to combine both frequency-based and context-based recommendation systems and quantify the intent of a user to provide better recommendations. We ran experiments on real-world timestamped user activity data, in the setting of recommending reports to the users of a business analytics tool application and the results are better than the baselines. We also tuned certain aspects of our model to arrive at optimized results.
Firstly, we have a content-based recommendation system which recommends particular kinds of items to the users. This consists of the recommendation system used by Netflix @cite_11 , traditional music recommendation systems @cite_12 and video recommendation by YouTube @cite_3 . These kinds of recommendations are driven by the content of the items to be recommended (i.e. description of the music, movie title, genre etc.) and a profile of the user's preference based on the historical information. A way of combining the global popularity and the user preference of videos to recommend the next N videos recommendation on YouTube is described in @cite_3 . Various methodologies like use of latent factors to model the relationship between items and users and to determine the rating of videos by the neighboring videos seen by the user are discussed in @cite_11 .
@cite_3 @cite_12 @cite_11
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We discuss the video recommendation system in use at YouTube, the world's most popular online video community. The system recommends personalized sets of videos to users based on their activity on the site. We discuss some of the unique challenges that the system faces and how we address them. In addition, we provide details on the experimentation and evaluation framework used to test and tune new algorithms. We also present some of the findings from these experiments. This article outlines the overall strategy and summarizes a few key innovations of the team that won the first Netflix progress prize.