Fast context-aware recommendations with factorization machines

Abstract

The situation in which a choice is made is an important information for recommender systems. Context-aware recommenders take this information into account to make predictions. So far, the best performing method for contextaware rating prediction in terms of predictive accuracy is Multiverse Recommendation based on the Tucker tensor factorization model. However this method has two drawbacks:(1) its model complexity is exponential in the number of context variables and polynomial in the size of the factorization and (2) it only works for categorical context variables. On the other hand there is a large variety of fast but specialized recommender methods which lack the generality of contextaware methods.We propose to apply Factorization Machines (FMs) to model contextual information and to provide context-aware rating predictions. This approach results in fast contextaware recommendations because the model equation of FMs can be computed in linear time both in the number of context variables and the factorization size. For learning FMs, we develop an iterative optimization method that analytically finds the least-square solution for one parameter given the other ones. Finally, we show empirically that our approach outperforms Multiverse Recommendation in prediction quality and runtime.

Context-Aware Recommendations with Random Partition Factorization Machines

“…In contrast to the previous two methods, the contextual modeling method uses all the contextual and user-item information simultaneously to make predictions. More recent works have focused on the third method [10,13,24,27]. Karatzoglou et al [10] proposed Multiverse Recommendation model in which the different types of context are considered as additional dimensions in the representation of the data as a tensor.…”

“…However, for real-world scenarios its computational complexity is too high. Rendle [24] showed that Factorization Machines (FM) model can be applied to context-aware recommendation because that a wide variety of context-aware data can be transformed into prediction task using real-valued feature vectors. Nguyen et al [16] developed a nonlinear probabilistic algorithm for context-aware recommendation using Gaussian processes which is called Gaussian Process Factorization Machines (GPFM).…”

“…The SGD algorithm for FM has a linear computational and constant storage complexity [21]. (2) Alternating least-squares (ALS) algorithms that iteratively solves a least-squares problem per model parameter and updates each model parameter with the optimal solution [24]. Here, V is f  p matrix of which f is the dimensionality of factor vectors and p ¼ n 0 þ n 1 þ :::n mÀ1 .…”

“…However, the dataset contains user's age and gender features. Just like [24], we also follow [10] and apply their method to generate modified dataset. In other words, we modify the original Yahoo!…”

Flexible Query Answering Systems 2015

“…With the prevalence of smart mobile devices [6], the consumers' clickstream data have been enriched with various contextual information [25], such as geographical information, which poses significant new opportunities as well as challenges [13]. Some data mining techniques have been employed to extract consumers' context-aware preferences [12,20]. However, these research efforts mostly focused on purchase records while ignoring the search activities.…”