Collaborative Filtering for Implicit Feedback Datasets

“An alternative strategy, our focus in this work, relies only on past user behavior without requiring the creation of explicit profiles. This approach is known as Collaborative Filtering (CF), a term coined by the developers of the first recommender system - Tapestry [8]. CF analyzes relationships between users and interdependencies among products, in order to identify new user-item associations. For example, some CF systems identify pairs of items that tend to be rated similarly or like-minded users with similar history of rating or purchasing to deduce unknown relationships between users and items. The only required information is the past behavior of users, which might be their previous transactions or the way they rate products. A major appeal of CF is that it is domain free, yet it can address aspects of the data that are often elusive and very difficult to profile using content based techniques. While generally being more accurate than content based techniques, CF suffers from the cold start problem, due to its inability to address products new to the system, for which content based approaches would be adequate.

Recommender systems rely on different types of input. Most convenient is the high quality explicit feedback, which includes explicit input by users regarding their interest in products. For example, Netflix collects star ratings for movies and TiVo users indicate their preferences for TV shows by hitting thumbs-up/down buttons. However, explicit feedback is not always available. Thus, recommenders can infer user preferences from the more abundant implicit feedback, which indirectly reflect opinion through observing user behavior [14]. Types of implicit feedback include purchase history, browsing history, search patterns, or even mouse movements. For example, a user that purchased many books by the same author probably likes that author.

The vast majority of the literature in the field is focused on processing explicit feedback; probably thanks to the convenience of using this kind of pure information. However, in many practical situations recommender systems need to be centered on implicit feedback. This may reflect reluctance of users to rate products, or limitations of the system that is unable to collect explicit feedback. In an implicit model, once the user gives approval to collect usage data, no additional explicit feedback (e.g. ratings) is required on the user’s part.”

“The numerical value of explicit feedback indicates preference, whereas the numerical value of implicit feedback indicates confidence.”

“In this work we studied collaborative filtering on datasets with implicit feedback, which is a very common situation. One of our main findings is that implicit user observations should be transformed into two paired magnitudes: preferences and confidence levels. In other words, for each user-item pair, we derive from the input data an estimate to whether the user would like or dislike the item (“preference”) and couple this estimate with a confidence level. This preference-confidence partition has no parallel in the widely studied explicit-feedback datasets, yet serves a key role in analyzing implicit feedback.”