Abstract
As one of the most popular and successfully applied recommendation methods, collaborative filtering aims to extract low-dimensional user and item representation from historic user-item interaction matrix. The similarity between the user and item representation vectors in the same space well measures the degree of interest and thus can be directly used for recommendation. This paper proposes to leverage the emerging deep neural language model to solve the collaborative filtering-based multimedia recommendation problem. By applying the standard Word2Vec model on the user-item interaction data, we can obtain the item embedding representation. Based on this, three strategies are introduced to derive the user embedded representation in the same space, which exploit both the user-item interaction and the correlation among items. Experiments on article recommendation application demonstrate that the proposed deep neural language models achieve superior performance than the traditional collaborative filtering methods based on matrix factorization and topic model.
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Notes
Successful recommendation of long-item items is very important to enhance the user loyalty and contributes significantly to a practical recommender. The long-tail items are analog to the low-frequency words in neural embedding. Many neural embedding methods simply drop the low-frequency words. A possible solution is to first ignore the low-frequency words while training and then predict the probability of low-frequency words with their context [16].
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Liu, Y., Li, L. & Liu, J. Bilateral neural embedding for collaborative filtering-based multimedia recommendation. Multimed Tools Appl 77, 12533–12544 (2018). https://doi.org/10.1007/s11042-017-4902-8
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DOI: https://doi.org/10.1007/s11042-017-4902-8