We propose Meta-Prod2vec, a novel method to compute item similarities for recommendation that leverages existing item metadata. Such scenarios are frequently encountered in applications such as content recommendation, ad targeting and web search. Our method leverages past user interactions with items and their attributes to compute low-dimensional embeddings of items. Specifically, the item metadata is injected into the model as side information to regularize the item embeddings. We show that the new item representations lead to better performance on recommendation tasks on an open music dataset.
Existing methods for recommender systems can roughly be categorized into collaborative filtering (CF) based methods, content-based (CB) methods and hybrid methods. CF-based methods @cite_8 are based on user's interaction with items, such as clicks, and don't require domain knowledge. Content-based methods make use of the user or product content profiles. In practice, CF methods are more popular because they can discover interesting associations between products without requiring the heavy knowledge collection needed by the content-based methods. However, CF methods suffer from cold-start problem in which no or few interaction are available with niche or new items in the system. In recent years, more sophisticated methods, namely latent factor models, have been developed to address the data sparsity problem of CF methods which we will discuss in Section . To further help overcoming cold-start problem, recent works focused on developing hybrid methods by combining latent factor models with content information which we will cover in Section .
@cite_8
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This paper focuses on developing effective and efficient algorithms for top-N recommender systems. A novel Sparse Linear Method (SLIM) is proposed, which generates top-N recommendations by aggregating from user purchase rating profiles. A sparse aggregation coefficient matrix W is learned from SLIM by solving an 1-norm and 2-norm regularized optimization problem. W is demonstrated to produce high quality recommendations and its sparsity allows SLIM to generate recommendations very fast. A comprehensive set of experiments is conducted by comparing the SLIM method and other state-of-the-art top-N recommendation methods. The experiments show that SLIM achieves significant improvements both in run time performance and recommendation quality over the best existing methods.