Abstract
Recommender systems can benefit from a plethora of signals influencing user behavior such as her past interactions, her social connections, as well as the similarity between different items. However, existing methods are challenged when taking all this data into account and often do not exploit all available information. This is primarily due to the fact that it is non-trivial to combine the various information as they mutually influence each other. To address this shortcoming, here, we propose a ‘Fusion Recommender’ (FuseRec), which models each of these factors separately and later combines them in an interpretable manner. We find this general framework to yield compelling results on all three investigated datasets, Epinions, Ciao, and CiaoDVD, outperforming the state-of-the-art by more than 14% for Ciao and Epinions. In addition, we provide a detailed ablation study, showing that our combined model achieves accurate results, often better than any of its components individually. Our model also provides insights on the importance of each of the factors in different datasets.
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Experiments with time-sensitive item embeddings decreased accuracy of the reported results.
\({\hat{i}} = W[i]\) where i represents the item index and \(W \in R^{\vert {\mathcal {I}} \vert X D}\)
\({\hat{u}} = W[u]\) where i represents the item index and \(W \in R^{\vert {\mathcal {U}} \vert X D}\)
Both Ciao and Epinions datasets are available at www.cse.msu.edu/~tangjili/trust.html.
Dataset available from www.librec.net/datasets.html.
We also experimented with constraining each training session to comprise of just a single item, but that resulted in slightly worse performance.
We also evaluated other intervals, but they all performed similarly.
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Narang, K., Song, Y., Schwing, A. et al. FuseRec: fusing user and item homophily modeling with temporal recommender systems. Data Min Knowl Disc 35, 837–862 (2021). https://doi.org/10.1007/s10618-021-00738-8
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DOI: https://doi.org/10.1007/s10618-021-00738-8