Recommender systems serve as bridges between users and items by recommending items to users that they might find interesting. Collaborative filtering (CF) is a technique commonly used in recommender systems. It predicts a user’s preference for an item based on past user-item interactions. A common form of this past interaction is called implicit feedback in which we record the user consumption behavior (click/buy/watch etc.) when they interact with the items. The simplicity of implicit feedback brings the challenge of the sparseness of the signal. Specifically, it is positive-only feedback since it only contains the positive signal of a user consuming an item. With such data, a valuable piece of information that can be used for making recommendations is the co-occurrence of ite...[ Read more ]

Recommender systems serve as bridges between users and items by recommending items to users that they might find interesting. Collaborative filtering (CF) is a technique commonly used in recommender systems. It predicts a user’s preference for an item based on past user-item interactions. A common form of this past interaction is called implicit feedback in which we record the user consumption behavior (click/buy/watch etc.) when they interact with the items. The simplicity of implicit feedback brings the challenge of the sparseness of the signal. Specifically, it is positive-only feedback since it only contains the positive signal of a user consuming an item. With such data, a valuable piece of information that can be used for making recommendations is the co-occurrence of items or users; that is, two items being co-consumed by users or two users co-consuming the same item.

In this thesis, we explore the role of co-occurrence in implicit feedback recommendation. In the first part, we show that efficient co-occurrence estimation can lead to improved recommendations by two popular recommenders. We show that the memory-based recommenders rely on co-occurrence estimation but due to the finite sample size, this estimation is noisy. Using insights from Marchenko-Pastur law we remove this noise by clipping small eigenvalues of the co-occurrence matrix. Also, we can shrink the largest eigenvalue to remove the "global" effects of the system. Both these cleaning strategies lead to better co-occurrence estimation, and this is translated into more accurate and diverse recommendations.

In the second part, we introduce methods that further exploit the co-occurrence information by building models on top of the item co-occurrence. We introduce the notion of multi-dimensional user clustering, where each dimension is a group of co-occurring items. We present two methods to perform this multi-dimensional user clustering. Unlike existing latent vector methods, the resulting models learn interpretable latent dimensions that lend themselves easily for explanations. In addition, they exhibit a better warm and cold start performance.

In the third part, we introduce structure learning for deep learning based implicit feedback recommenders. We use the item co-occurrence to learn the structure of auto-encoder based recommenders. We first find overlapping item groups based on item co-occurrence. These overlapping groups are then used as the skeleton of the structure for the encoder and decoder of an auto-encoder. The resulting sparse structure can be seen as a structural prior for network training and it guides the parameter estimation. This leads to improved performance over the state-of-the-art deep learning based recommenders due to a smaller spectral norm of the weight matrices and hence a better generalization performance.

Finally, we explore the case when additional features information is also available with implicit feedback. When a user consumes an item we can treat their features as co-occurring. However, the existing methods model all feature co-occurrence. Moreover, they model each of these feature co-occurrences using the same function. We propose a neural architecture search based approach to search for which feature interactions to model and how to model these interactions. The results show that this approach outperforms the state-of-the-art feature interaction based recommenders using a fraction of the parameters and flops and it learns meaningful feature co-occurrences.