Neural Collaborative Filtering
“In the era of information explosion, recommender systems play a pivotal role in alleviating information overload, having been widely adopted by many online services, including E-commerce, online news and social media sites. The key to a personalized recommender system is in modelling users' preference on items based on their past interactions (e.g., ratings and clicks), known as collaborative filtering [31, 46]. Among the various collaborative filtering techniques, matrix factorization (MF) [14, 21] is the most popular one, which projects users and items into a shared latent space, using a vector of latent features to represent a user or an item. Thereafter a user's interaction on an item is modelled as the inner product of their latent vectors.
Popularized by the Net ix Prize, MF has become the de facto approach to latent factor model-based recommendation. Much research effort has been devoted to enhancing MF, such as integrating it with neighbor-based models [21], combining it with topic models of item content [38], and ex- tending it to factorization machines [26] for a generic modelling of features. Despite the effectiveness of MF for collaborative filtering, it is well-known that its performance can be hindered by the simple choice of the interaction function | inner product. For example, for the task of rating prediction on explicit feedback, it is well known that the performance of the MF model can be improved by incorporating user and item bias terms into the interaction function1. While it seems to be just a trivial tweak for the inner product operator [14], it points to the positive effect of designing a better, dedicated interaction function for modelling the la- tent feature interactions between users and items. The inner product, which simply combines the multiplication of latent features linearly, may not be sufficient to capture the com- plex structure of user interaction data.”
“This work addresses the aforementioned research problems by formalizing a neural network modelling approach for collaborative filtering. We focus on implicit feedback, which indirectly reflects users' preference through behaviours like watching videos, purchasing products and clicking items. Compared to explicit feedback (i.e., ratings and reviews), implicit feedback can be tracked automatically and is thus much easier to collect for content providers. However, it is more challenging to utilize, since user satisfaction is not observed and there is a natural scarcity of negative feedback. In this paper, we explore the central theme of how to utilize DNNs to model noisy implicit feedback signals. The main contributions of this work are as follows.
We present a neural network architecture to model latent features of users and items and devise a general framework NCF for collaborative filtering based on neural networks.
We show that MF can be interpreted as a specialization of NCF and utilize a multi-layer perceptron to endow NCF modelling with a high level of non-linearities.
We perform extensive experiments on two real-world datasets to demonstrate the effectiveness of our NCF approaches and the promise of deep learning for collaborative filtering.”
“Here a value of 1 for yui indicates that there is an interaction between user u and item i; however, it does not mean u actually likes i. Similarly, a value of 0 does not necessarily mean u does not like i, it can be that the user is not aware of the item. This poses challenges in learning from implicit data, since it provides only noisy signals about users' preference. While observed entries at least re ect users' interest on items, the unobserved entries can be just missing data and there is a natural scarcity of negative feedback.
The recommendation problem with implicit feedback is formulated as the problem of estimating the scores of unobserved entries in Y, which are used for ranking the items.”
“As we can see, MF models the two-way interaction of user and item latent factors, assuming each dimension of the latent space is independent of each other and linearly combining them with the same weight. As such, MF can be deemed as a linear model of latent factors.”