Abstract
Traditional news media Web sites usually provide generic recommendations that are not personalized to the preferences of their users. Typically, news recommendation algorithms mainly rely on the long-term preferences of users and do not adjust their model to the continuous stream of short-lived incoming stories to capture short-term intentions revealed by users’ sessions. In this paper, we therefore study the problem of session-aware recommendations by running random walks on dynamic heterogeneous graphs. Concretely, we construct a heterogeneous information network consisting of users, news articles, news categories, locations and sessions. By using different (1) sliding time window sizes, (2) sub-graphs for model learning, (3) sequential article weighting strategies and (4) more diversified random walks, we perform recommendations in a second step. Our algorithm proposal is evaluated on three real-life data sets, and we demonstrate that our method outperforms state-of-the-art methods by delivering more accurate and diversified recommendations.
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Notes
Note, that in Table 2 we depict a transposed result matrix, that is row-normalized, where element (i, j) defines the transition probability from node i to node j.
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Symeonidis, P., Kirjackaja, L. & Zanker, M. Session-aware news recommendations using random walks on time-evolving heterogeneous information networks. User Model User-Adap Inter 30, 727–755 (2020). https://doi.org/10.1007/s11257-020-09261-9
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DOI: https://doi.org/10.1007/s11257-020-09261-9